Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

INVALID_ARGUMENT: Input to reshape is a tensor with X values, but the requested shape requires a multiple of Y #1177

Open
etale-cohomology opened this issue Jan 3, 2023 · 2 comments
Open

INVALID_ARGUMENT: Input to reshape is a tensor with X values, but the requested shape requires a multiple of Y #1177

etale-cohomology opened this issue Jan 3, 2023 · 2 comments

Comments

@etale-cohomology
Copy link

etale-cohomology commented Jan 3, 2023 •
edited
Loading

I'm training EfficientDet3 on a custom dataset with COCO annotations having 585 categories and 125 images (as a test, although there are more images), as follows:

rm -rf tfrecord  &&  mkdir -p tfrecord
PYTHONPATH=".:$PYTHONPATH"  py dataset/create_coco_tfrecord.py --image_dir=../testaug --object_annotations_file=../testaug/tmp/test_coco.json --output_file_prefix=tfrecord/val --num_shards=32

rm -rf ./tmp/efficientdet-d3-finetune
py main.py                                   \
	--mode=train                                 \
	--train_file_pattern=tfrecord/val*.tfrecord  \
	--val_file_pattern=tfrecord/val*.tfrecord    \
	--model_name=efficientdet-d3                 \
	--model_dir=./tmp/efficientdet-d3-finetune   \
	--ckpt=efficientdet-d3                       \
	--train_batch_size=2                         \
	--eval_batch_size=2                          \
	--num_examples_per_epoch=125                 \
	--num_epochs=1                               \
	--hparams=config.yaml

with the following config.yaml file:

num_classes: 586
lr_warmup_init: 0.08
learning_rate: 0.8
var_freeze_expr: '(efficientnet|fpn_cells|resample_p6)'
label_map: {1: Abel_NOT_Rec, 2: Abel_WHI_SinSco_12Yo_BUT_700ml_00_00, 3: Abel_WHI_SinSco_12Yo_BUT_700ml_01_00, 4: Abel_WHI_SinSco_12Yo_KAR_2SZK_700ml_00_00, 5: Abel_WHI_SinSco_12Yo_KAR_2SZK_700ml_01_00, 6: Abel_WHI_SinSco_12Yo_TUB_700ml_00_00, 7: Abel_WHI_SinSco_12Yo_TUB_700ml_01_00, 8: Abel_WHI_SinSco_14Yo_BUT_700ml_00_00, 9: Abel_WHI_SinSco_14Yo_TUB_700ml_00_00, 10: Abel_WHI_SinSco_16Yo_BUT_700ml_00_00, 11: Abel_WHI_SinSco_16Yo_BUT_700ml_01_00, 12: Abel_WHI_SinSco_16Yo_TUB_700ml_00_00, 13: Abel_WHI_SinSco_16Yo_TUB_700ml_01_00, 14: Abel_WHI_SinSco_18Yo_BUT_500ml_00_00, 15: Abel_WHI_SinSco_18Yo_BUT_500ml_01_00, 16: Abel_WHI_SinSco_18Yo_BUT_700ml_00_00, 17: Abel_WHI_SinSco_18Yo_BUT_700ml_01_00, 18: Abel_WHI_SinSco_18Yo_TUB_500ml_00_00, 19: Abel_WHI_SinSco_18Yo_TUB_500ml_01_00, 20: Abel_WHI_SinSco_18Yo_TUB_700ml_00_00, 21: Abel_WHI_SinSco_18Yo_TUB_700ml_01_00, 22: AbelAbuna_WHI_SinSco_BUT_700ml_00_00, 23: AbelAbuna_WHI_SinSco_BUT_700ml_01_00, 24: AbelAbuna_WHI_SinSco_TUB_700ml_00_00, 25: AbelAbuna_WHI_SinSco_TUB_700ml_01_00, 26: AbelCasgAnn_WHI_SinSco_BUT_700ml_00_00, 27: AbelCasgAnn_WHI_SinSco_BUT_700ml_01_00, 28: AbelCasgAnn_WHI_SinSco_TUB_700ml_00_00, 29: AbelCasgAnn_WHI_SinSco_TUB_700ml_01_00, 30: Absu_NOT_Rec, 31: Absu_WOD_Czy_BUT_1000ml_00_00, 32: Absu_WOD_Czy_BUT_1000ml_01_00, 33: Absu_WOD_Czy_BUT_4500ml_00_00, 34: Absu_WOD_Czy_BUT_500ml_00_00, 35: Absu_WOD_Czy_BUT_500ml_01_00, 36: Absu_WOD_Czy_BUT_50ml_00_00, 37: Absu_WOD_Czy_BUT_700ml_00_00, 38: Absu_WOD_Czy_BUT_700ml_01_00, 39: Absu_WOD_Czy_KAR_2KIE_700ml_00_00, 40: AbsuElyx_WOD_Czy_BUT_1000ml_00_00, 41: AbsuElyx_WOD_Czy_BUT_1750ml_00_00, 42: AbsuElyx_WOD_Czy_BUT_700ml_00_00, 43: AbsuElyx_WOD_Czy_BUT_700ml_01_00, 44: AbsuExtra_WOD_Sma_BUT_700ml_00_00, 45: AbsuGrape_WOD_Sma_BUT_700ml_00_00, 46: AbsuKuran_WOD_Sma_BUT_700ml_00_00, 47: AbsuKuran_WOD_Sma_BUT_700ml_01_00, 48: AbsuLime_WOD_Sma_BUT_700ml_00_00, 49: AbsuLime_WOD_Sma_BUT_700ml_01_00, 50: AbsuPears_WOD_Sma_BUT_700ml_00_00, 51: Ara_BRA_3Yo_BUT_500ml_00_00, 52: Ara_BRA_3Yo_BUT_500ml_01_00, 53: Ara_BRA_3Yo_BUT_700ml_00_00, 54: Ara_BRA_3Yo_BUT_700ml_01_00, 55: Ara_BRA_3Yo_KAR_500ml_00_00, 56: Ara_BRA_3Yo_KAR_500ml_01_00, 57: Ara_BRA_3Yo_KAR_700ml_00_00, 58: Ara_BRA_3Yo_KAR_700ml_01_00, 59: Ara_BRA_5Yo_BUT_500ml_01_00, 60: Ara_BRA_5Yo_BUT_700ml_00_00, 61: Ara_BRA_5Yo_BUT_700ml_01_00, 62: Ara_BRA_5Yo_KAR_500ml_00_00, 63: Ara_BRA_5Yo_KAR_500ml_01_00, 64: Ara_BRA_5Yo_KAR_700ml_00_00, 65: Ara_BRA_5Yo_KAR_700ml_01_00, 66: Ara_NOT_Rec, 67: AraAkhta_BRA_10Yo_BUT_700ml_00_00, 68: AraAkhta_BRA_10Yo_BUT_700ml_01_00, 69: AraAkhta_BRA_10Yo_KAR_700ml_00_00, 70: AraAkhta_BRA_10Yo_KAR_700ml_01_00, 71: AraAni_BRA_6Yo_BUT_700ml_00_00, 72: AraAni_BRA_6Yo_KAR_700ml_00_00, 73: AraDvin_BRA_BUT_700ml_00_00, 74: AraDvin_BRA_BUT_700ml_01_00, 75: AraDvin_BRA_KAR_700ml_00_00, 76: AraDvin_BRA_KAR_700ml_01_00, 77: AraNairi_BRA_20Yo_BUT_700ml_00_00, 78: AraNairi_BRA_20Yo_BUT_700ml_01_00, 79: AraNairi_BRA_20Yo_KAR_700ml_00_00, 80: AraNairi_BRA_20Yo_KAR_700ml_01_00, 81: AraOtbor_BRA_7Yo_BUT_700ml_00_00, 82: AraOtbor_BRA_7Yo_KAR_700ml_00_00, 83: AraVaspu_BRA_15Yo_BUT_700ml_00_00, 84: AraVaspu_BRA_15Yo_BUT_700ml_01_00, 85: AraVaspu_BRA_15Yo_KAR_700ml_00_00, 86: AraVaspu_BRA_15Yo_KAR_700ml_01_00, 87: Ball_NOT_Rec, 88: Ball_WHI_BleSco_12Yo_BUT_700ml_00_00, 89: Ball_WHI_BleSco_12Yo_BUT_700ml_01_00, 90: Ball_WHI_BleSco_12Yo_KAR_2SZK_700ml_00_00, 91: Ball_WHI_BleSco_12Yo_KAR_2SZK_700ml_01_00, 92: Ball_WHI_BleSco_12Yo_KAR_2SZK_700ml_03_00, 93: Ball_WHI_BleSco_12Yo_KAR_700ml_00_00, 94: Ball_WHI_BleSco_12Yo_KAR_700ml_02_00, 95: Ball_WHI_BleSco_12Yo_KAR_PIE_700ml_00_00, 96: Ball_WHI_BleSco_12Yo_KAR_PIE_700ml_01_00, 97: Ball_WHI_BleSco_12Yo_KAR_PIE_700ml_02_00, 98: Ball_WHI_BleSco_17Yo_BUT_700ml_00_00, 99: Ball_WHI_BleSco_17Yo_BUT_700ml_01_00, 100: Ball_WHI_BleSco_17Yo_KAR_700ml_00_00, 101: Ball_WHI_BleSco_17Yo_KAR_700ml_01_00, 102: Ball_WHI_BleSco_21Yo_BUT_700ml_00_00, 103: Ball_WHI_BleSco_21Yo_BUT_700ml_01_00, 104: Ball_WHI_BleSco_21Yo_KAR_700ml_00_00, 105: Ball_WHI_BleSco_21Yo_KAR_700ml_01_00, 106: Ball_WHI_BleSco_21Yo_KAR_700ml_02_00, 107: Ball_WHI_BleSco_30Yo_BUT_700ml_00_00, 108: Ball_WHI_BleSco_30Yo_BUT_700ml_01_00, 109: Ball_WHI_BleSco_30Yo_KAR_700ml_00_00, 110: Ball_WHI_BleSco_30Yo_KAR_700ml_01_00, 111: Ball_WHI_BleSco_30Yo_KAR_700ml_02_00, 112: Ball_WHI_BleSco_40Yo_BUT_700ml_00_00, 113: Ball_WHI_BleSco_40Yo_SKR_700ml_00_00, 114: Ball_WHI_BleSco_7Yo_BUT_700ml_00_00, 115: BallBrasi_WHI_BleSco_BUT_700ml_00_00, 116: BallFines_WHI_BleSco_BUT_1000ml_00_00, 117: BallFines_WHI_BleSco_BUT_1500ml_00_00, 118: BallFines_WHI_BleSco_BUT_200ml_00_00, 119: BallFines_WHI_BleSco_BUT_4500ml_00_00, 120: BallFines_WHI_BleSco_BUT_500ml_00_00, 121: BallFines_WHI_BleSco_BUT_50ml_00_00, 122: BallFines_WHI_BleSco_BUT_700ml_00_00, 123: BallFines_WHI_BleSco_BUT_700ml_01_00, 124: BallFines_WHI_BleSco_BUT_700ml_02_00, 125: BallFines_WHI_BleSco_KAR_SZK_700ml_01_00, 126: BallFines_WHI_BleSco_KAR_SZK_700ml_02_00, 127: BallFines_WHI_BleSco_KAR_SZKMIN_700ml_00_00, 128: BallGlenb_WHI_SinSco_15Yo_BUT_700ml_00_00, 129: BallGlenb_WHI_SinSco_15Yo_TUB_700ml_00_00, 130: BallGlent_WHI_SinSco_15Yo_BUT_700ml_00_00, 131: BallGlent_WHI_SinSco_15Yo_TUB_700ml_00_00, 132: BallMilto_WHI_SinSco_15Yo_BUT_700ml_00_00, 133: BallMilto_WHI_SinSco_15Yo_TUB_700ml_00_00, 134: BallPassi_WHI_BleSco_BUT_700ml_00_00, 135: BallWild_WHI_BleSco_BUT_700ml_00_00, 136: Bec_LIK_BUT_500ml_00_00, 137: Bec_LIK_BUT_500ml_01_00, 138: Bec_LIK_BUT_700ml_00_00, 139: Bec_LIK_BUT_700ml_01_00, 140: Bec_NOT_Rec, 141: Bee24_GIN_BUT_700ml_00_00, 142: Bee24_GIN_BUT_700ml_01_00, 143: Bee_GIN_BUT_700ml_00_00, 144: Bee_GIN_BUT_700ml_01_00, 145: Bee_GIN_BUT_700ml_02_00, 146: Bee_GIN_BUT_700ml_03_00, 147: Bee_NOT_Rec, 148: BeeBloodOra_GIN_BUT_700ml_00_00, 149: BeePink_GIN_BUT_700ml_00_00, 150: BeePink_GIN_BUT_700ml_01_00, 151: BeePink_GIN_BUT_700ml_02_00, 152: BraOfGle_NOT_Rec, 153: BraOfGle_WHI_SinSco_25Yo_BUT_700ml_00_00, 154: BraOfGle_WHI_SinSco_25Yo_KAR_700ml_00_00, 155: Bum_NOT_Rec, 156: BumCream_RUM_BUT_700ml_00_00, 157: BumOrigi_RUM_BUT_350ml_00_00, 158: BumOrigi_RUM_BUT_700ml_00_00, 159: BumXo_RUM_BUT_700ml_00_00, 160: CamVie_NOT_Rec, 161: CamVie_WIN_PolSloBia_BUT_750ml_00_00, 162: CamVie_WIN_PolSloBia_BUT_750ml_01_00, 163: CamVie_WIN_PolSloBia_BUT_750ml_02_00, 164: CamVieGranRes_WIN_WytCze_BUT_750ml_00_00, 165: CamVieReser_WIN_WytCze_BUT_750ml_00_00, 166: CamVieTempr_WIN_WytCze_BUT_750ml_00_00, 167: CanSpeOld_NOT_Rec, 168: CanSpeOld_WHI_BleKan_BUT_700ml_00_00, 169: ChiReg_NOT_Rec, 170: ChiReg_WHI_BleSco_12Yo_BUT_1000ml_00_00, 171: ChiReg_WHI_BleSco_12Yo_BUT_1000ml_01_00, 172: ChiReg_WHI_BleSco_12Yo_BUT_1000ml_02_00, 173: ChiReg_WHI_BleSco_12Yo_BUT_1500ml_00_00, 174: ChiReg_WHI_BleSco_12Yo_BUT_1500ml_01_00, 175: ChiReg_WHI_BleSco_12Yo_BUT_200ml_00_00, 176: ChiReg_WHI_BleSco_12Yo_BUT_200ml_01_00, 177: ChiReg_WHI_BleSco_12Yo_BUT_4500ml_00_00, 178: ChiReg_WHI_BleSco_12Yo_BUT_4500ml_01_00, 179: ChiReg_WHI_BleSco_12Yo_BUT_500ml_00_00, 180: ChiReg_WHI_BleSco_12Yo_BUT_500ml_01_00, 181: ChiReg_WHI_BleSco_12Yo_BUT_500ml_02_00, 182: ChiReg_WHI_BleSco_12Yo_BUT_50ml_00_00, 183: ChiReg_WHI_BleSco_12Yo_BUT_50ml_01_00, 184: ChiReg_WHI_BleSco_12Yo_BUT_700ml_00_00, 185: ChiReg_WHI_BleSco_12Yo_BUT_700ml_02_00, 186: ChiReg_WHI_BleSco_12Yo_KAR_1000ml_15_00, 187: ChiReg_WHI_BleSco_12Yo_KAR_1000ml_16_00, 188: ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_01_00, 189: ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_03_00, 190: ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_05_00, 191: ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_08_00, 192: ChiReg_WHI_BleSco_12Yo_KAR_700ml_00_00, 193: ChiReg_WHI_BleSco_12Yo_KAR_700ml_01_00, 194: ChiReg_WHI_BleSco_12Yo_KAR_700ml_07_00, 195: ChiReg_WHI_BleSco_12Yo_KAR_700ml_08_00, 196: ChiReg_WHI_BleSco_12Yo_KAR_700ml_15_00, 197: ChiReg_WHI_BleSco_18Yo_BUT_700ml_00_00, 198: ChiReg_WHI_BleSco_18Yo_KAR_2SZK_700ml_01_00, 199: ChiReg_WHI_BleSco_18Yo_KAR_700ml_01_00, 200: ChiReg_WHI_BleSco_18Yo_KAR_700ml_02_00, 201: ChiReg_WHI_BleSco_18Yo_KAR_700ml_03_00, 202: ChiReg_WHI_BleSco_25Yo_BUT_700ml_00_00, 203: ChiReg_WHI_BleSco_25Yo_KAR_700ml_00_00, 204: ChiReg_WHI_BleSco_25Yo_KAR_700ml_01_00, 205: ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_BUT_700ml_00_00, 206: ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_BUT_700ml_02_00, 207: ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_KAR_700ml_00_00, 208: ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_KAR_700ml_02_00, 209: ChiRegExtraShe_WHI_BleSco_13Yo_BUT_700ml_00_00, 210: ChiRegExtraShe_WHI_BleSco_13Yo_BUT_700ml_01_00, 211: ChiRegExtraShe_WHI_BleSco_13Yo_KAR_700ml_00_00, 212: ChiRegExtraShe_WHI_BleSco_13Yo_KAR_700ml_02_00, 213: ChiRegMizun_WHI_BleSco_BUT_700ml_00_00, 214: ChiRegMizun_WHI_BleSco_KAR_700ml_00_00, 215: ChiRegUltis_WHI_BleSco_BUT_700ml_00_00, 216: ChiRegUltis_WHI_BleSco_KAR_700ml_00_00, 217: ChiRegXv_WHI_BleSco_15Yo_BUT_700ml_00_00, 218: ChiRegXv_WHI_BleSco_15Yo_BUT_700ml_01_00, 219: ChiRegXv_WHI_BleSco_15Yo_KAR_700ml_00_00, 220: ChiRegXv_WHI_BleSco_15Yo_KAR_700ml_01_00, 221: ChiRegXv_WHI_BleSco_15Yo_KAR_700ml_02_00, 222: DeKuy_NOT_Rec, 223: DeKuyAmare_LIK_BUT_700ml_01_00, 224: DeKuyBlueCur_LIK_BUT_500ml_00_00, 225: DeKuyBlueCur_LIK_BUT_700ml_00_00, 226: DeKuyCremeDeCas_LIK_BUT_700ml_00_00, 227: DeKuyTriplSec_LIK_BUT_500ml_00_00, 228: DeKuyTriplSec_LIK_BUT_700ml_00_00, 229: DeKuyTriplSec_LIK_BUT_700ml_01_00, 230: FouRos_NOT_Rec, 231: FouRosSinglBar_WHI_Bou_BUT_700ml_00_00, 232: FouRosSmallBat_WHI_Bou_BUT_700ml_00_00, 233: GleKei_NOT_Rec, 234: GleKei_WHI_SinSco_21Yo_BUT_700ml_00_00, 235: GleKei_WHI_SinSco_21Yo_KAR_700ml_00_00, 236: Gol_NOT_Rec, 237: Gol_RUM_BUT_200ml_00_00, 238: Gol_RUM_BUT_200ml_01_00, 239: Gol_RUM_BUT_500ml_00_00, 240: Gol_RUM_BUT_500ml_01_00, 241: Hav_NOT_Rec, 242: HavClub_RUM_3Yo_BUT_700ml_00_00, 243: HavClub_RUM_3Yo_BUT_700ml_01_00, 244: HavClub_RUM_7Yo_BUT_700ml_00_00, 245: HavClubEsp_RUM_BUT_700ml_00_00, 246: HavClubEsp_RUM_BUT_700ml_01_00, 247: HavClubSelDeMae_RUM_BUT_700ml_01_00, 248: HavClubSelDeMae_RUM_BUT_700ml_02_00, 249: HavClubSelDeMae_RUM_KAR_700ml_00_00, 250: HavClubSelDeMae_RUM_TUB_700ml_00_00, 251: HavClubSelDeMae_RUM_TUB_700ml_01_00, 252: JacCre_NOT_Rec, 253: JacCreChard_WIN_WytBia_BUT_750ml_00_00, 254: JacCreChard_WIN_WytBia_BUT_750ml_01_00, 255: JacCreChardPinNoi_WIN_MusWytBia_BUT_750ml_00_00, 256: JacCreChardPinNoi_WIN_MusWytBia_BUT_750ml_01_00, 257: JacCreChardPinNoi_WIN_MusWytBia_BUT_750ml_02_00, 258: JacCreChardRes_WIN_WytBia_BUT_750ml_00_00, 259: JacCreChardRes_WIN_WytBia_BUT_750ml_01_00, 260: JacCreDoublBarCabSav_WIN_WytCze_BUT_750ml_00_00, 261: JacCreDoublBarCabSav_WIN_WytCze_BUT_750ml_01_00, 262: JacCreDoublBarShi_WIN_WytCze_BUT_750ml_00_00, 263: JacCreDoublBarShi_WIN_WytCze_BUT_750ml_01_00, 264: JacCreMerloShi_WIN_PolSloCze_BUT_750ml_00_00, 265: JacCreMerloShi_WIN_PolSloCze_BUT_750ml_01_00, 266: JacCreMerloShi_WIN_PolSloCze_BUT_750ml_02_00, 267: JacCreMoscaRos_WIN_SloRoz_BUT_750ml_00_00, 268: JacCreMoscaRos_WIN_SloRoz_BUT_750ml_01_00, 269: JacCreMoscaRos_WIN_SloRoz_BUT_750ml_02_00, 270: JacCreRiesl_WIN_WytBia_BUT_750ml_00_00, 271: JacCreSauviBla_WIN_PolWytBia_BUT_750ml_00_00, 272: JacCreSauviBla_WIN_PolWytBia_BUT_750ml_01_00, 273: JacCreSauviBla_WIN_PolWytBia_BUT_750ml_02_00, 274: JacCreSauviMosTwiPic_WIN_MusWytBia_BUT_750ml_00_00, 275: JacCreShiraCab_WIN_WytCze_BUT_750ml_00_00, 276: JacCreShiraCab_WIN_WytCze_BUT_750ml_01_00, 277: JacCreShiraCab_WIN_WytCze_BUT_750ml_02_00, 278: JacCreShiraGre_WIN_PolWytCze_BUT_750ml_00_00, 279: JacCreShiraGre_WIN_PolWytCze_BUT_750ml_01_00, 280: JacCreShiraGre_WIN_PolWytCze_BUT_750ml_02_00, 281: JacCreShiraRes_WIN_WytCze_BUT_750ml_00_00, 282: JacCreShiraRes_WIN_WytCze_BUT_750ml_01_00, 283: JacCreShiraRos_WIN_PolWytRoz_BUT_750ml_00_00, 284: JacCreSparkMos_WIN_MusSloBia_BUT_750ml_01_00, 285: JacCreStillMos_WIN_SloBia_BUT_750ml_00_00, 286: JacCreStillMos_WIN_SloBia_BUT_750ml_01_00, 287: JacCreStillMos_WIN_SloBia_BUT_750ml_02_00, 288: JacCreStillMos_WIN_SloBia_BUT_750ml_03_00, 289: JacCreStillMos_WIN_SloBia_BUT_750ml_04_00, 290: Jam_NOT_Rec, 291: Jam_WHI_BleIri_BUT_1000ml_00_00, 292: Jam_WHI_BleIri_BUT_200ml_00_00, 293: Jam_WHI_BleIri_BUT_200ml_01_00, 294: Jam_WHI_BleIri_BUT_500ml_00_00, 295: Jam_WHI_BleIri_BUT_700ml_00_00, 296: Jam_WHI_BleIri_BUT_700ml_01_00, 297: Jam_WHI_BleIri_BUT_STO_4500ml_00_00, 298: Jam_WHI_BleIri_KAR_2SZK_700ml_00_00, 299: Jam_WHI_BleIri_KAR_MIN_700ml_00_00, 300: Jam_WHI_BleIri_KAR_MIN_700ml_01_00, 301: Jam_WHI_BleIri_KAR_STO_4500ml_00_00, 302: JamBlackBar_WHI_BleIri_BUT_700ml_00_00, 303: JamBlackBar_WHI_BleIri_BUT_700ml_01_00, 304: JamBlackBar_WHI_BleIri_KAR_700ml_00_00, 305: JamBlackBar_WHI_BleIri_KAR_700ml_01_00, 306: JamBlendDog_WHI_BleIri_BUT_700ml_00_00, 307: JamCaskmIpa_WHI_BleIri_BUT_700ml_00_00, 308: JamCaskmIpa_WHI_BleIri_BUT_700ml_01_00, 309: JamCaskmIpa_WHI_BleIri_BUT_700ml_02_00, 310: JamCaskmIpa_WHI_BleIri_KAR_700ml_00_00, 311: JamCaskmIpa_WHI_BleIri_KAR_700ml_01_00, 312: JamCaskmSto_WHI_BleIri_BUT_700ml_00_00, 313: JamCaskmSto_WHI_BleIri_BUT_700ml_01_00, 314: JamCaskmSto_WHI_BleIri_BUT_700ml_02_00, 315: JamCaskmSto_WHI_BleIri_BUT_700ml_03_00, 316: JamCaskmSto_WHI_BleIri_KAR_700ml_00_00, 317: JamCaskmSto_WHI_BleIri_KAR_700ml_01_00, 318: JamCoopeCro_WHI_BleIri_BUT_700ml_00_00, 319: JamCrest_WHI_BleIri_BUT_700ml_00_00, 320: JamCrest_WHI_BleIri_BUT_700ml_01_00, 321: JamCrest_WHI_BleIri_KAR_700ml_00_00, 322: JamCrest_WHI_BleIri_KAR_700ml_01_00, 323: JamDistiSaf_WHI_BleIri_BUT_700ml_00_00, 324: JamOrang_WHI_BleIri_BUT_700ml_00_00, 325: Kah_LIK_BUT_700ml_00_00, 326: Kah_LIK_BUT_700ml_01_00, 327: Kah_LIK_BUT_700ml_02_00, 328: Kah_NOT_Rec, 329: Lil_NOT_Rec, 330: LilBlanc_LIK_BUT_750ml_01_00, 331: LilBlanc_LIK_BUT_750ml_02_00, 332: LilRose_LIK_BUT_750ml_00_00, 333: LilRose_LIK_BUT_750ml_01_00, 334: Lon_NOT_Rec, 335: Lon_WHI_SinSco_16Yo_KAR_700ml_00_00, 336: LonTheDisCho_WHI_SinSco_BUT_700ml_00_00, 337: LonTheDisCho_WHI_SinSco_KAR_700ml_00_00, 338: Luk_NOT_Rec, 339: Luk_WOD_Czy_BUT_1000ml_00_00, 340: Luk_WOD_Czy_BUT_1000ml_01_00, 341: Luk_WOD_Czy_BUT_200ml_00_00, 342: Luk_WOD_Czy_BUT_200ml_01_00, 343: Luk_WOD_Czy_BUT_500ml_00_00, 344: Luk_WOD_Czy_BUT_500ml_01_00, 345: Luk_WOD_Czy_BUT_700ml_00_00, 346: Luk_WOD_Czy_BUT_700ml_01_00, 347: LukWisni_WOD_Sma_BUT_500ml_00_00, 348: Mal_LIK_BUT_500ml_00_00, 349: Mal_LIK_BUT_700ml_00_00, 350: Mal_LIK_BUT_700ml_01_00, 351: Mal_LIK_BUT_700ml_02_00, 352: Mal_NOT_Rec, 353: Malf_NOT_Rec, 354: MalfConAra_GIN_BUT_700ml_00_00, 355: MalfConLim_GIN_BUT_700ml_00_00, 356: MalfOrigiIta_GIN_BUT_700ml_00_00, 357: MalfRosaIta_GIN_BUT_700ml_00_00, 358: MalLime_LIK_BUT_700ml_00_00, 359: MalPassiFru_LIK_BUT_700ml_00_00, 360: MalPassiFru_LIK_BUT_700ml_01_00, 361: MalPassiFru_LIK_BUT_700ml_02_00, 362: Mar_NOT_Rec, 363: MarBlueSwi_KON_BUT_700ml_00_00, 364: MarBlueSwi_KON_KAR_700ml_00_00, 365: MarCordoBle_KON_BUT_700ml_00_00, 366: MarCordoBle_KON_KAR_700ml_00_00, 367: MarLor_KON_BUT_700ml_00_00, 368: MarLor_KON_TUB_700ml_00_00, 369: MarMedai_KON_BUT_700ml_00_00, 370: MarMedai_KON_BUT_700ml_02_00, 371: MarMedai_KON_BUT_700ml_03_00, 372: MarMedai_KON_KAR_700ml_00_00, 373: MarMedai_KON_KAR_700ml_02_00, 374: MarMedai_KON_KAR_700ml_03_00, 375: MarMedai_KON_KAR_700ml_04_00, 376: MarMedai_KON_KAR_700ml_05_00, 377: MarMedai_KON_KAR_700ml_06_00, 378: MarVs_KON_BUT_700ml_00_00, 379: MarVs_KON_BUT_700ml_01_00, 380: MarVs_KON_KAR_2KIE_700ml_01_00, 381: MarVs_KON_KAR_700ml_00_00, 382: MarVs_KON_KAR_700ml_01_00, 383: MarVsopRedBar_KON_KAR_2SZK_700ml_00_00, 384: MarVsopRedBar_KON_KAR_2SZK_700ml_01_00, 385: MarVsopRedBar_KON_KAR_2SZK_700ml_02_00, 386: MarVsSinDis_KON_BUT_700ml_00_00, 387: MarVsSinDis_KON_KAR_700ml_00_00, 388: MarVsSinDis_KON_KAR_700ml_01_00, 389: MarVsSinDis_KON_KAR_700ml_02_00, 390: MarVsSinDis_KON_KAR_KIE_700ml_00_00, 391: MarXo_KON_BUT_700ml_00_00, 392: MarXo_KON_BUT_700ml_01_00, 393: MarXo_KON_KAR_700ml_00_00, 394: Mon47_NOT_Rec, 395: Mon47Dry_GIN_BUT_500ml_00_00, 396: Mon47Sloe_GIN_BUT_500ml_00_00, 397: Mum_NOT_Rec, 398: MumDemiSec_SZA_BUT_750ml_00_00, 399: MumDemiSec_SZA_BUT_750ml_01_00, 400: MumGrandCor_SZA_BUT_750ml_00_00, 401: MumGrandCor_SZA_KAR_2KIE_750ml_00_00, 402: MumGrandCor_SZA_KAR_750ml_00_00, 403: MumGrandCorRos_SZA_BUT_750ml_00_00, 404: MumIceExt_SZA_BUT_750ml_00_00, 405: MumOlymp_SZA_BUT_750ml_01_00, 406: MumRose_SZA_BUT_750ml_00_00, 407: MumRose_SZA_BUT_750ml_01_00, 408: MumRouge_SZA_BUT_1500ml_00_00, 409: MumRouge_SZA_BUT_3000ml_00_00, 410: MumRouge_SZA_BUT_375ml_00_00, 411: MumRouge_SZA_BUT_375ml_01_00, 412: MumRouge_SZA_BUT_750ml_00_00, 413: MumRouge_SZA_BUT_750ml_01_00, 414: MumRouge_SZA_KAR_750ml_00_00, 415: MumRouge_SZA_KAR_750ml_02_00, 416: MumRouge_SZA_KAR_750ml_03_00, 417: MumRouge_SZA_KAR_750ml_05_00, 418: MumRouge_SZA_KAR_750ml_06_00, 419: MumRouge_SZA_SKR_3000ml_00_00, 420: Olm_NOT_Rec, 421: OlmAltosPla_TEQ_BUT_700ml_00_00, 422: OlmAltosRep_TEQ_BUT_700ml_00_00, 423: OlmBlanc_TEQ_BUT_700ml_00_00, 424: OlmBlanc_TEQ_BUT_700ml_01_00, 425: OlmBlanc_TEQ_BUT_700ml_02_00, 426: OlmGold_TEQ_BUT_700ml_00_00, 427: OlmGold_TEQ_BUT_700ml_01_00, 428: OlmGold_TEQ_BUT_700ml_02_00, 429: Ost_NOT_Rec, 430: Ost_WOD_Czy_BUT_1000ml_00_00, 431: Ost_WOD_Czy_BUT_1750ml_00_00, 432: Ost_WOD_Czy_BUT_3000ml_00_00, 433: Ost_WOD_Czy_BUT_500ml_00_00, 434: Ost_WOD_Czy_BUT_700ml_00_00, 435: Ost_WOD_Czy_KAR_700ml_00_00, 436: Ost_WOD_Czy_KAR_KIEMIO_700ml_00_00, 437: OstBlack_WOD_Czy_BUT_700ml_00_00, 438: OstVap_WOD_Czy_KAR_700ml_00_00, 439: PanTad_NOT_Rec, 440: PanTad_WOD_Czy_BUT_500ml_00_00, 441: PanTad_WOD_Czy_BUT_500ml_01_00, 442: PanTad_WOD_Czy_BUT_500ml_02_00, 443: PanTad_WOD_Czy_BUT_500ml_03_00, 444: PanTad_WOD_Czy_BUT_500ml_05_00, 445: PanTad_WOD_Czy_BUT_700ml_00_00, 446: PanTad_WOD_Czy_BUT_700ml_01_00, 447: PanTad_WOD_Czy_BUT_700ml_02_00, 448: PanTad_WOD_Czy_BUT_700ml_03_00, 449: PanTad_WOD_Czy_BUT_700ml_06_00, 450: PanTadAroni_WOD_Sma_BUT_500ml_00_00, 451: PanTadAroni_WOD_Sma_BUT_500ml_01_00, 452: PanTadAroni_WOD_Sma_BUT_500ml_02_00, 453: PanTadAroni_WOD_Sma_BUT_500ml_03_00, 454: PanTadPrzep_WOD_Sma_BUT_500ml_00_00, 455: PanTadPrzep_WOD_Sma_BUT_500ml_01_00, 456: PanTadPrzep_WOD_Sma_BUT_500ml_02_00, 457: PanTadPrzep_WOD_Sma_BUT_500ml_03_00, 458: PanZosSlonyKar_LIK_BUT_500ml_00_00, 459: Pas_NOT_Rec, 460: Pas_WHI_BleSco_BUT_1000ml_00_00, 461: Pas_WHI_BleSco_BUT_1000ml_01_00, 462: Pas_WHI_BleSco_BUT_200ml_00_00, 463: Pas_WHI_BleSco_BUT_500ml_00_00, 464: Pas_WHI_BleSco_BUT_500ml_01_00, 465: Pas_WHI_BleSco_BUT_700ml_00_00, 466: Pas_WHI_BleSco_BUT_700ml_01_00, 467: PerJou_NOT_Rec, 468: PerJouBelleEpo_SZA_BUT_750ml_00_00, 469: PerJouBelleEpo_SZA_BUT_750ml_01_00, 470: PerJouBelleEpo_SZA_KAR_750ml_00_00, 471: PerJouGrandBru_SZA_BUT_750ml_00_00, 472: PerJouGrandBru_SZA_BUT_750ml_01_00, 473: PerJouGrandBru_SZA_KAR_750ml_01_00, 474: Pols_NOT_Rec, 475: PolsWisni_WOD_Sma_BUT_200ml_00_00, 476: PolsWisni_WOD_Sma_BUT_500ml_00_00, 477: Red_NOT_Rec, 478: Red_WHI_SinIri_12Yo_BUT_700ml_00_00, 479: Red_WHI_SinIri_12Yo_BUT_700ml_01_00, 480: Red_WHI_SinIri_12Yo_KAR_700ml_00_00, 481: Red_WHI_SinIri_12Yo_KAR_700ml_01_00, 482: Red_WHI_SinIri_21Yo_BUT_700ml_00_00, 483: Red_WHI_SinIri_21Yo_KAR_700ml_00_00, 484: Ric_LIK_BUT_700ml_00_00, 485: Ric_LIK_BUT_700ml_01_00, 486: Ric_NOT_Rec, 487: Robocza, 488: RoySal_NOT_Rec, 489: RoySal_WHI_BleSco_21Yo_BUT_700ml_00_00, 490: RoySal_WHI_BleSco_21Yo_BUT_700ml_01_00, 491: RoySal_WHI_BleSco_21Yo_KAR_700ml_00_00, 492: RoySal_WHI_BleSco_21Yo_KAR_700ml_01_00, 493: RoySalDiamoTri_WHI_BleSco_BUT_700ml_00_00, 494: RoySalDiamoTri_WHI_BleSco_KAR_700ml_00_00, 495: Sca_NOT_Rec, 496: ScaGlans_WHI_SinSco_BUT_700ml_00_00, 497: ScaGlans_WHI_SinSco_KAR_700ml_00_00, 498: ScaSkire_WHI_SinSco_BUT_700ml_00_00, 499: ScaSkire_WHI_SinSco_KAR_700ml_00_00, 500: SeaGin_GIN_BUT_350ml_01_00, 501: SeaGin_GIN_BUT_700ml_01_00, 502: SeaGin_NOT_Rec, 503: SeaGinLime_GIN_BUT_350ml_01_00, 504: SeaGinLime_GIN_BUT_700ml_01_00, 505: Siw_NOT_Rec, 506: Siw_WOD_Sma_BUT_500ml_00_00, 507: Siw_WOD_Sma_BUT_500ml_01_00, 508: TheGle_NOT_Rec, 509: TheGle_WHI_SinSco_12Yo_BUT_50ml_00_00, 510: TheGle_WHI_SinSco_12Yo_BUT_700ml_00_00, 511: TheGle_WHI_SinSco_12Yo_BUT_700ml_01_00, 512: TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_00_00, 513: TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_01_00, 514: TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_02_00, 515: TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_03_00, 516: TheGle_WHI_SinSco_12Yo_KAR_700ml_00_00, 517: TheGle_WHI_SinSco_12Yo_KAR_700ml_01_00, 518: TheGle_WHI_SinSco_12Yo_KAR_SKA_700ml_00_00, 519: TheGle_WHI_SinSco_15Yo_BUT_700ml_00_00, 520: TheGle_WHI_SinSco_15Yo_BUT_700ml_01_00, 521: TheGle_WHI_SinSco_15Yo_KAR_2SZK_700ml_00_00, 522: TheGle_WHI_SinSco_15Yo_KAR_700ml_00_00, 523: TheGle_WHI_SinSco_15Yo_KAR_700ml_01_00, 524: TheGle_WHI_SinSco_18Yo_BUT_700ml_00_00, 525: TheGle_WHI_SinSco_18Yo_BUT_700ml_01_00, 526: TheGle_WHI_SinSco_18Yo_KAR_700ml_00_00, 527: TheGle_WHI_SinSco_18Yo_KAR_700ml_01_00, 528: TheGle_WHI_SinSco_21Yo_BUT_700ml_00_00, 529: TheGle_WHI_SinSco_21Yo_KAR_700ml_00_00, 530: TheGle_WHI_SinSco_25Yo_BUT_700ml_00_00, 531: TheGle_WHI_SinSco_25Yo_SKR_700ml_00_00, 532: TheGleFoundRes_WHI_SinSco_BUT_700ml_00_00, 533: TheGleFoundRes_WHI_SinSco_BUT_700ml_01_00, 534: TheGleFoundRes_WHI_SinSco_KAR_700ml_00_00, 535: TheGleFoundRes_WHI_SinSco_KAR_700ml_01_00, 536: TheGleIllicSti_WHI_SinSco_12Yo_BUT_700ml_00_00, 537: TheGleLicenDra_WHI_SinSco_12Yo_BUT_700ml_00_00, 538: TheGleLicenDra_WHI_SinSco_12Yo_KAR_700ml_00_00, 539: TheGleSinglCasSheBut_WHI_SinSco_14Yo_BUT_700ml_00_00, 540: TheGleSinglCasSheBut_WHI_SinSco_14Yo_KAR_700ml_00_00, 541: TheGleSpect_WHI_SinSco_KAR_600ml_00_00, 542: Wyb_NOT_Rec, 543: Wyb_WOD_Czy_BUT_1000ml_00_00, 544: Wyb_WOD_Czy_BUT_200ml_00_00, 545: Wyb_WOD_Czy_BUT_500ml_00_00, 546: Wyb_WOD_Czy_BUT_50ml_00_00, 547: Wyb_WOD_Czy_BUT_700ml_00_00, 548: Wyb_WOD_Czy_BUT_700ml_01_00, 549: WybAgres_WOD_Sma_BUT_200ml_00_00, 550: WybAgres_WOD_Sma_BUT_500ml_00_00, 551: WybCzarnPor_WOD_Sma_BUT_200ml_00_00, 552: WybCzarnPor_WOD_Sma_BUT_500ml_00_00, 553: WybCzarnPor_WOD_Sma_BUT_500ml_01_00, 554: WybCzarnPor_WOD_Sma_BUT_500ml_02_00, 555: WybExqui_WOD_Czy_BUT_1750ml_00_00, 556: WybExqui_WOD_Czy_BUT_700ml_00_00, 557: WybExqui_WOD_Czy_KAR_700ml_00_00, 558: WybGrusz_WOD_Sma_BUT_200ml_00_00, 559: WybGrusz_WOD_Sma_BUT_500ml_00_00, 560: WybGrusz_WOD_Sma_BUT_500ml_01_00, 561: WybGrusz_WOD_Sma_BUT_500ml_02_00, 562: WybOdMis_WOD_Czy_BUT_500ml_00_00, 563: WybOdMis_WOD_Czy_BUT_700ml_00_00, 564: WybProst_WOD_Czy_BUT_500ml_00_00, 565: WybProst_WOD_Czy_BUT_500ml_01_00, 566: WybProst_WOD_Czy_BUT_500ml_02_00, 567: WybProst_WOD_Czy_BUT_700ml_00_00, 568: WybProst_WOD_Czy_BUT_700ml_01_00, 569: WybProst_WOD_Czy_BUT_700ml_02_00, 570: WybPszen_WOD_Czy_BUT_500ml_00_00, 571: WybPszen_WOD_Czy_BUT_500ml_01_00, 572: WybPszen_WOD_Czy_BUT_500ml_02_00, 573: WybSliwk_WOD_Sma_BUT_200ml_00_00, 574: WybSliwk_WOD_Sma_BUT_200ml_01_00, 575: WybSliwk_WOD_Sma_BUT_500ml_00_00, 576: WybSliwk_WOD_Sma_BUT_500ml_01_00, 577: WybSliwk_WOD_Sma_BUT_500ml_02_00, 578: WybWisni_WOD_Sma_BUT_200ml_00_00, 579: WybWisni_WOD_Sma_BUT_200ml_01_00, 580: WybWisni_WOD_Sma_BUT_500ml_00_00, 581: WybWisni_WOD_Sma_BUT_500ml_01_00, 582: WybWisni_WOD_Sma_BUT_500ml_02_00, 583: WybZiemn_WOD_Czy_BUT_500ml_00_00, 584: WybZiemn_WOD_Czy_BUT_500ml_01_00, 585: WybZiemn_WOD_Czy_BUT_500ml_02_00}

But then when I try to do inference as follows

py model_inspect.py --runmode=infer --model_name=efficientdet-d3   --ckpt_path=efficientdet-d3 --hparams=config.yaml  --output_image_dir=./  --input_image=63282abe23719c411f59cebd.jpg

I get the error:

2023-01-02 23:13:00.920668: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-02 23:13:01.769240: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9634 MB memory:  -> device: 0, name: TITAN V, pci bus id: 0000:04:00.0, compute capability: 7.0
WARNING:tensorflow:From /home/da/git/woj/efficientdet/utils.py:536: The name tf.keras.layers.enable_v2_dtype_behavior is deprecated. Please use tf.compat.v1.keras.layers.enable_v2_dtype_behavior instead.

W0102 23:13:02.096955 140179268290368 module_wrapper.py:149] From /home/da/git/woj/efficientdet/utils.py:536: The name tf.keras.layers.enable_v2_dtype_behavior is deprecated. Please use tf.compat.v1.keras.layers.enable_v2_dtype_behavior instead.

WARNING:tensorflow:From /home/da/py38/lib/python3.8/site-packages/tensorflow/python/training/moving_averages.py:548: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0102 23:13:20.483227 140179268290368 deprecation.py:337] From /home/da/py38/lib/python3.8/site-packages/tensorflow/python/training/moving_averages.py:548: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
2023-01-02 23:14:09.055171: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8204
Traceback (most recent call last):
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1377, in _do_call
    return fn(*args)
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1360, in _run_fn
    return self._call_tf_sessionrun(options, feed_dict, fetch_list,
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1453, in _call_tf_sessionrun
    return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) INVALID_ARGUMENT: Input to reshape is a tensor with 10160640 values, but the requested shape requires a multiple of 586
	 [[{{node Reshape}}]]
	 [[strided_slice_16/_4665]]
  (1) INVALID_ARGUMENT: Input to reshape is a tensor with 10160640 values, but the requested shape requires a multiple of 586
	 [[{{node Reshape}}]]
0 successful operations.
0 derived errors ignored.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "model_inspect.py", line 338, in <module>
    app.run(main)
  File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 308, in run
    _run_main(main, args)
  File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 254, in _run_main
    sys.exit(main(argv))
  File "model_inspect.py", line 332, in main
    inspector.run_model(FLAGS.runmode, input_image=FLAGS.input_image, output_image_dir=FLAGS.output_image_dir, input_video=FLAGS.input_video, output_video=FLAGS.output_video, line_thickness=FLAGS.line_thickness, max_boxes_to_draw=FLAGS.max_boxes_to_draw, min_score_thresh=FLAGS.min_score_thresh, nms_method=FLAGS.nms_method, bm_runs=FLAGS.bm_runs, threads=FLAGS.threads, trace_filename=FLAGS.trace_filename)
  File "model_inspect.py", line 321, in run_model
    elif runmode=='infer':              self.inference_single_image(kwargs['input_image'], kwargs['output_image_dir'], **config_dict)
  File "model_inspect.py", line 170, in inference_single_image
    driver.inference(image_image_path, output_dir, **kwargs)
  File "/home/da/git/woj/efficientdet/inference.py", line 566, in inference
    predictions = sess.run(detections_batch)
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 967, in run
    result = self._run(None, fetches, feed_dict, options_ptr,
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1190, in _run
    results = self._do_run(handle, final_targets, final_fetches,
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1370, in _do_run
    return self._do_call(_run_fn, feeds, fetches, targets, options,
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1396, in _do_call
    raise type(e)(node_def, op, message)  # pylint: disable=no-value-for-parameter
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:

Detected at node 'Reshape' defined at (most recent call last):
    File "model_inspect.py", line 338, in <module>
      app.run(main)
    File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 308, in run
      _run_main(main, args)
    File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 254, in _run_main
      sys.exit(main(argv))
    File "model_inspect.py", line 332, in main
      inspector.run_model(FLAGS.runmode, input_image=FLAGS.input_image, output_image_dir=FLAGS.output_image_dir, input_video=FLAGS.input_video, output_video=FLAGS.output_video, line_thickness=FLAGS.line_thickness, max_boxes_to_draw=FLAGS.max_boxes_to_draw, min_score_thresh=FLAGS.min_score_thresh, nms_method=FLAGS.nms_method, bm_runs=FLAGS.bm_runs, threads=FLAGS.threads, trace_filename=FLAGS.trace_filename)
    File "model_inspect.py", line 321, in run_model
      elif runmode=='infer':              self.inference_single_image(kwargs['input_image'], kwargs['output_image_dir'], **config_dict)
    File "model_inspect.py", line 170, in inference_single_image
      driver.inference(image_image_path, output_dir, **kwargs)
    File "/home/da/git/woj/efficientdet/inference.py", line 565, in inference
      detections_batch = det_post_process(params, class_outputs, box_outputs, scales)
    File "/home/da/git/woj/efficientdet/inference.py", line 196, in det_post_process
      else:                                 nms_boxes, nms_scores, nms_classes, _ = postprocess.postprocess_global(params, cls_outputs, box_outputs, scales)
    File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 395, in postprocess_global
      boxes, scores, classes = pre_nms(params, cls_outputs, box_outputs)
    File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 141, in pre_nms
      cls_outputs, box_outputs = merge_class_box_level_outputs(
    File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 77, in merge_class_box_level_outputs
      tf.reshape(cls_outputs[level], [batch_size, -1, params['num_classes']]))
Node: 'Reshape'
Detected at node 'Reshape' defined at (most recent call last):
    File "model_inspect.py", line 338, in <module>
      app.run(main)
    File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 308, in run
      _run_main(main, args)
    File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 254, in _run_main
      sys.exit(main(argv))
    File "model_inspect.py", line 332, in main
      inspector.run_model(FLAGS.runmode, input_image=FLAGS.input_image, output_image_dir=FLAGS.output_image_dir, input_video=FLAGS.input_video, output_video=FLAGS.output_video, line_thickness=FLAGS.line_thickness, max_boxes_to_draw=FLAGS.max_boxes_to_draw, min_score_thresh=FLAGS.min_score_thresh, nms_method=FLAGS.nms_method, bm_runs=FLAGS.bm_runs, threads=FLAGS.threads, trace_filename=FLAGS.trace_filename)
    File "model_inspect.py", line 321, in run_model
      elif runmode=='infer':              self.inference_single_image(kwargs['input_image'], kwargs['output_image_dir'], **config_dict)
    File "model_inspect.py", line 170, in inference_single_image
      driver.inference(image_image_path, output_dir, **kwargs)
    File "/home/da/git/woj/efficientdet/inference.py", line 565, in inference
      detections_batch = det_post_process(params, class_outputs, box_outputs, scales)
    File "/home/da/git/woj/efficientdet/inference.py", line 196, in det_post_process
      else:                                 nms_boxes, nms_scores, nms_classes, _ = postprocess.postprocess_global(params, cls_outputs, box_outputs, scales)
    File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 395, in postprocess_global
      boxes, scores, classes = pre_nms(params, cls_outputs, box_outputs)
    File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 141, in pre_nms
      cls_outputs, box_outputs = merge_class_box_level_outputs(
    File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 77, in merge_class_box_level_outputs
      tf.reshape(cls_outputs[level], [batch_size, -1, params['num_classes']]))
Node: 'Reshape'
2 root error(s) found.
  (0) INVALID_ARGUMENT: Input to reshape is a tensor with 10160640 values, but the requested shape requires a multiple of 586
	 [[{{node Reshape}}]]
	 [[strided_slice_16/_4665]]
  (1) INVALID_ARGUMENT: Input to reshape is a tensor with 10160640 values, but the requested shape requires a multiple of 586
	 [[{{node Reshape}}]]
0 successful operations.
0 derived errors ignored.

Original stack trace for 'Reshape':
  File "model_inspect.py", line 338, in <module>
    app.run(main)
  File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 308, in run
    _run_main(main, args)
  File "/home/da/py38/lib/python3.8/site-packages/absl/app.py", line 254, in _run_main
    sys.exit(main(argv))
  File "model_inspect.py", line 332, in main
    inspector.run_model(FLAGS.runmode, input_image=FLAGS.input_image, output_image_dir=FLAGS.output_image_dir, input_video=FLAGS.input_video, output_video=FLAGS.output_video, line_thickness=FLAGS.line_thickness, max_boxes_to_draw=FLAGS.max_boxes_to_draw, min_score_thresh=FLAGS.min_score_thresh, nms_method=FLAGS.nms_method, bm_runs=FLAGS.bm_runs, threads=FLAGS.threads, trace_filename=FLAGS.trace_filename)
  File "model_inspect.py", line 321, in run_model
    elif runmode=='infer':              self.inference_single_image(kwargs['input_image'], kwargs['output_image_dir'], **config_dict)
  File "model_inspect.py", line 170, in inference_single_image
    driver.inference(image_image_path, output_dir, **kwargs)
  File "/home/da/git/woj/efficientdet/inference.py", line 565, in inference
    detections_batch = det_post_process(params, class_outputs, box_outputs, scales)
  File "/home/da/git/woj/efficientdet/inference.py", line 196, in det_post_process
    else:                                 nms_boxes, nms_scores, nms_classes, _ = postprocess.postprocess_global(params, cls_outputs, box_outputs, scales)
  File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 395, in postprocess_global
    boxes, scores, classes = pre_nms(params, cls_outputs, box_outputs)
  File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 141, in pre_nms
    cls_outputs, box_outputs = merge_class_box_level_outputs(
  File "/home/da/git/woj/efficientdet/tf2/postprocess.py", line 77, in merge_class_box_level_outputs
    tf.reshape(cls_outputs[level], [batch_size, -1, params['num_classes']]))
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py", line 150, in error_handler
    return fn(*args, **kwargs)
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py", line 1082, in op_dispatch_handler
    return dispatch_target(*args, **kwargs)
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 194, in reshape
    result = gen_array_ops.reshape(tensor, shape, name)
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/ops/gen_array_ops.py", line 8546, in reshape
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/framework/op_def_library.py", line 740, in _apply_op_helper
    op = g._create_op_internal(op_type_name, inputs, dtypes=None,
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 3776, in _create_op_internal
    ret = Operation(
  File "/home/da/py38/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 2175, in __init__
    self._traceback = tf_stack.extract_stack_for_node(self._c_op)

Although the following works:

py model_inspect.py --runmode=infer --model_name=efficientdet-d3   --ckpt_path=./tmp/efficientdet-d3-finetune --hparams=config.yaml  --output_image_dir=./  --input_image=63282abe23719c411f59cebd.jpg

(The difference is in the ckpt_path.)

What's going on?
@etale-cohomology
Copy link
Author

Here's the output of train.py

I0102 23:06:55.323895 140562995341120 main.py:164] {'name': 'efficientdet-d3', 'act_type': 'swish', 'image_size': (896, 896), 'target_size': None, 'input_rand_hflip': True, 'jitter_min': 0.1, 'jitter_max': 2.0, 'autoaugment_policy': None, 'grid_mask': False, 'sample_image': None, 'map_freq': 5, 'num_classes': 586, 'seg_num_classes': 3, 'heads': ['object_detection'], 'skip_crowd_during_training': True, 'label_map': {1: 'Abel_NOT_Rec', 2: 'Abel_WHI_SinSco_12Yo_BUT_700ml_00_00', 3: 'Abel_WHI_SinSco_12Yo_BUT_700ml_01_00', 4: 'Abel_WHI_SinSco_12Yo_KAR_2SZK_700ml_00_00', 5: 'Abel_WHI_SinSco_12Yo_KAR_2SZK_700ml_01_00', 6: 'Abel_WHI_SinSco_12Yo_TUB_700ml_00_00', 7: 'Abel_WHI_SinSco_12Yo_TUB_700ml_01_00', 8: 'Abel_WHI_SinSco_14Yo_BUT_700ml_00_00', 9: 'Abel_WHI_SinSco_14Yo_TUB_700ml_00_00', 10: 'Abel_WHI_SinSco_16Yo_BUT_700ml_00_00', 11: 'Abel_WHI_SinSco_16Yo_BUT_700ml_01_00', 12: 'Abel_WHI_SinSco_16Yo_TUB_700ml_00_00', 13: 'Abel_WHI_SinSco_16Yo_TUB_700ml_01_00', 14: 'Abel_WHI_SinSco_18Yo_BUT_500ml_00_00', 15: 'Abel_WHI_SinSco_18Yo_BUT_500ml_01_00', 16: 'Abel_WHI_SinSco_18Yo_BUT_700ml_00_00', 17: 'Abel_WHI_SinSco_18Yo_BUT_700ml_01_00', 18: 'Abel_WHI_SinSco_18Yo_TUB_500ml_00_00', 19: 'Abel_WHI_SinSco_18Yo_TUB_500ml_01_00', 20: 'Abel_WHI_SinSco_18Yo_TUB_700ml_00_00', 21: 'Abel_WHI_SinSco_18Yo_TUB_700ml_01_00', 22: 'AbelAbuna_WHI_SinSco_BUT_700ml_00_00', 23: 'AbelAbuna_WHI_SinSco_BUT_700ml_01_00', 24: 'AbelAbuna_WHI_SinSco_TUB_700ml_00_00', 25: 'AbelAbuna_WHI_SinSco_TUB_700ml_01_00', 26: 'AbelCasgAnn_WHI_SinSco_BUT_700ml_00_00', 27: 'AbelCasgAnn_WHI_SinSco_BUT_700ml_01_00', 28: 'AbelCasgAnn_WHI_SinSco_TUB_700ml_00_00', 29: 'AbelCasgAnn_WHI_SinSco_TUB_700ml_01_00', 30: 'Absu_NOT_Rec', 31: 'Absu_WOD_Czy_BUT_1000ml_00_00', 32: 'Absu_WOD_Czy_BUT_1000ml_01_00', 33: 'Absu_WOD_Czy_BUT_4500ml_00_00', 34: 'Absu_WOD_Czy_BUT_500ml_00_00', 35: 'Absu_WOD_Czy_BUT_500ml_01_00', 36: 'Absu_WOD_Czy_BUT_50ml_00_00', 37: 'Absu_WOD_Czy_BUT_700ml_00_00', 38: 'Absu_WOD_Czy_BUT_700ml_01_00', 39: 'Absu_WOD_Czy_KAR_2KIE_700ml_00_00', 40: 'AbsuElyx_WOD_Czy_BUT_1000ml_00_00', 41: 'AbsuElyx_WOD_Czy_BUT_1750ml_00_00', 42: 'AbsuElyx_WOD_Czy_BUT_700ml_00_00', 43: 'AbsuElyx_WOD_Czy_BUT_700ml_01_00', 44: 'AbsuExtra_WOD_Sma_BUT_700ml_00_00', 45: 'AbsuGrape_WOD_Sma_BUT_700ml_00_00', 46: 'AbsuKuran_WOD_Sma_BUT_700ml_00_00', 47: 'AbsuKuran_WOD_Sma_BUT_700ml_01_00', 48: 'AbsuLime_WOD_Sma_BUT_700ml_00_00', 49: 'AbsuLime_WOD_Sma_BUT_700ml_01_00', 50: 'AbsuPears_WOD_Sma_BUT_700ml_00_00', 51: 'Ara_BRA_3Yo_BUT_500ml_00_00', 52: 'Ara_BRA_3Yo_BUT_500ml_01_00', 53: 'Ara_BRA_3Yo_BUT_700ml_00_00', 54: 'Ara_BRA_3Yo_BUT_700ml_01_00', 55: 'Ara_BRA_3Yo_KAR_500ml_00_00', 56: 'Ara_BRA_3Yo_KAR_500ml_01_00', 57: 'Ara_BRA_3Yo_KAR_700ml_00_00', 58: 'Ara_BRA_3Yo_KAR_700ml_01_00', 59: 'Ara_BRA_5Yo_BUT_500ml_01_00', 60: 'Ara_BRA_5Yo_BUT_700ml_00_00', 61: 'Ara_BRA_5Yo_BUT_700ml_01_00', 62: 'Ara_BRA_5Yo_KAR_500ml_00_00', 63: 'Ara_BRA_5Yo_KAR_500ml_01_00', 64: 'Ara_BRA_5Yo_KAR_700ml_00_00', 65: 'Ara_BRA_5Yo_KAR_700ml_01_00', 66: 'Ara_NOT_Rec', 67: 'AraAkhta_BRA_10Yo_BUT_700ml_00_00', 68: 'AraAkhta_BRA_10Yo_BUT_700ml_01_00', 69: 'AraAkhta_BRA_10Yo_KAR_700ml_00_00', 70: 'AraAkhta_BRA_10Yo_KAR_700ml_01_00', 71: 'AraAni_BRA_6Yo_BUT_700ml_00_00', 72: 'AraAni_BRA_6Yo_KAR_700ml_00_00', 73: 'AraDvin_BRA_BUT_700ml_00_00', 74: 'AraDvin_BRA_BUT_700ml_01_00', 75: 'AraDvin_BRA_KAR_700ml_00_00', 76: 'AraDvin_BRA_KAR_700ml_01_00', 77: 'AraNairi_BRA_20Yo_BUT_700ml_00_00', 78: 'AraNairi_BRA_20Yo_BUT_700ml_01_00', 79: 'AraNairi_BRA_20Yo_KAR_700ml_00_00', 80: 'AraNairi_BRA_20Yo_KAR_700ml_01_00', 81: 'AraOtbor_BRA_7Yo_BUT_700ml_00_00', 82: 'AraOtbor_BRA_7Yo_KAR_700ml_00_00', 83: 'AraVaspu_BRA_15Yo_BUT_700ml_00_00', 84: 'AraVaspu_BRA_15Yo_BUT_700ml_01_00', 85: 'AraVaspu_BRA_15Yo_KAR_700ml_00_00', 86: 'AraVaspu_BRA_15Yo_KAR_700ml_01_00', 87: 'Ball_NOT_Rec', 88: 'Ball_WHI_BleSco_12Yo_BUT_700ml_00_00', 89: 'Ball_WHI_BleSco_12Yo_BUT_700ml_01_00', 90: 'Ball_WHI_BleSco_12Yo_KAR_2SZK_700ml_00_00', 91: 'Ball_WHI_BleSco_12Yo_KAR_2SZK_700ml_01_00', 92: 'Ball_WHI_BleSco_12Yo_KAR_2SZK_700ml_03_00', 93: 'Ball_WHI_BleSco_12Yo_KAR_700ml_00_00', 94: 'Ball_WHI_BleSco_12Yo_KAR_700ml_02_00', 95: 'Ball_WHI_BleSco_12Yo_KAR_PIE_700ml_00_00', 96: 'Ball_WHI_BleSco_12Yo_KAR_PIE_700ml_01_00', 97: 'Ball_WHI_BleSco_12Yo_KAR_PIE_700ml_02_00', 98: 'Ball_WHI_BleSco_17Yo_BUT_700ml_00_00', 99: 'Ball_WHI_BleSco_17Yo_BUT_700ml_01_00', 100: 'Ball_WHI_BleSco_17Yo_KAR_700ml_00_00', 101: 'Ball_WHI_BleSco_17Yo_KAR_700ml_01_00', 102: 'Ball_WHI_BleSco_21Yo_BUT_700ml_00_00', 103: 'Ball_WHI_BleSco_21Yo_BUT_700ml_01_00', 104: 'Ball_WHI_BleSco_21Yo_KAR_700ml_00_00', 105: 'Ball_WHI_BleSco_21Yo_KAR_700ml_01_00', 106: 'Ball_WHI_BleSco_21Yo_KAR_700ml_02_00', 107: 'Ball_WHI_BleSco_30Yo_BUT_700ml_00_00', 108: 'Ball_WHI_BleSco_30Yo_BUT_700ml_01_00', 109: 'Ball_WHI_BleSco_30Yo_KAR_700ml_00_00', 110: 'Ball_WHI_BleSco_30Yo_KAR_700ml_01_00', 111: 'Ball_WHI_BleSco_30Yo_KAR_700ml_02_00', 112: 'Ball_WHI_BleSco_40Yo_BUT_700ml_00_00', 113: 'Ball_WHI_BleSco_40Yo_SKR_700ml_00_00', 114: 'Ball_WHI_BleSco_7Yo_BUT_700ml_00_00', 115: 'BallBrasi_WHI_BleSco_BUT_700ml_00_00', 116: 'BallFines_WHI_BleSco_BUT_1000ml_00_00', 117: 'BallFines_WHI_BleSco_BUT_1500ml_00_00', 118: 'BallFines_WHI_BleSco_BUT_200ml_00_00', 119: 'BallFines_WHI_BleSco_BUT_4500ml_00_00', 120: 'BallFines_WHI_BleSco_BUT_500ml_00_00', 121: 'BallFines_WHI_BleSco_BUT_50ml_00_00', 122: 'BallFines_WHI_BleSco_BUT_700ml_00_00', 123: 'BallFines_WHI_BleSco_BUT_700ml_01_00', 124: 'BallFines_WHI_BleSco_BUT_700ml_02_00', 125: 'BallFines_WHI_BleSco_KAR_SZK_700ml_01_00', 126: 'BallFines_WHI_BleSco_KAR_SZK_700ml_02_00', 127: 'BallFines_WHI_BleSco_KAR_SZKMIN_700ml_00_00', 128: 'BallGlenb_WHI_SinSco_15Yo_BUT_700ml_00_00', 129: 'BallGlenb_WHI_SinSco_15Yo_TUB_700ml_00_00', 130: 'BallGlent_WHI_SinSco_15Yo_BUT_700ml_00_00', 131: 'BallGlent_WHI_SinSco_15Yo_TUB_700ml_00_00', 132: 'BallMilto_WHI_SinSco_15Yo_BUT_700ml_00_00', 133: 'BallMilto_WHI_SinSco_15Yo_TUB_700ml_00_00', 134: 'BallPassi_WHI_BleSco_BUT_700ml_00_00', 135: 'BallWild_WHI_BleSco_BUT_700ml_00_00', 136: 'Bec_LIK_BUT_500ml_00_00', 137: 'Bec_LIK_BUT_500ml_01_00', 138: 'Bec_LIK_BUT_700ml_00_00', 139: 'Bec_LIK_BUT_700ml_01_00', 140: 'Bec_NOT_Rec', 141: 'Bee24_GIN_BUT_700ml_00_00', 142: 'Bee24_GIN_BUT_700ml_01_00', 143: 'Bee_GIN_BUT_700ml_00_00', 144: 'Bee_GIN_BUT_700ml_01_00', 145: 'Bee_GIN_BUT_700ml_02_00', 146: 'Bee_GIN_BUT_700ml_03_00', 147: 'Bee_NOT_Rec', 148: 'BeeBloodOra_GIN_BUT_700ml_00_00', 149: 'BeePink_GIN_BUT_700ml_00_00', 150: 'BeePink_GIN_BUT_700ml_01_00', 151: 'BeePink_GIN_BUT_700ml_02_00', 152: 'BraOfGle_NOT_Rec', 153: 'BraOfGle_WHI_SinSco_25Yo_BUT_700ml_00_00', 154: 'BraOfGle_WHI_SinSco_25Yo_KAR_700ml_00_00', 155: 'Bum_NOT_Rec', 156: 'BumCream_RUM_BUT_700ml_00_00', 157: 'BumOrigi_RUM_BUT_350ml_00_00', 158: 'BumOrigi_RUM_BUT_700ml_00_00', 159: 'BumXo_RUM_BUT_700ml_00_00', 160: 'CamVie_NOT_Rec', 161: 'CamVie_WIN_PolSloBia_BUT_750ml_00_00', 162: 'CamVie_WIN_PolSloBia_BUT_750ml_01_00', 163: 'CamVie_WIN_PolSloBia_BUT_750ml_02_00', 164: 'CamVieGranRes_WIN_WytCze_BUT_750ml_00_00', 165: 'CamVieReser_WIN_WytCze_BUT_750ml_00_00', 166: 'CamVieTempr_WIN_WytCze_BUT_750ml_00_00', 167: 'CanSpeOld_NOT_Rec', 168: 'CanSpeOld_WHI_BleKan_BUT_700ml_00_00', 169: 'ChiReg_NOT_Rec', 170: 'ChiReg_WHI_BleSco_12Yo_BUT_1000ml_00_00', 171: 'ChiReg_WHI_BleSco_12Yo_BUT_1000ml_01_00', 172: 'ChiReg_WHI_BleSco_12Yo_BUT_1000ml_02_00', 173: 'ChiReg_WHI_BleSco_12Yo_BUT_1500ml_00_00', 174: 'ChiReg_WHI_BleSco_12Yo_BUT_1500ml_01_00', 175: 'ChiReg_WHI_BleSco_12Yo_BUT_200ml_00_00', 176: 'ChiReg_WHI_BleSco_12Yo_BUT_200ml_01_00', 177: 'ChiReg_WHI_BleSco_12Yo_BUT_4500ml_00_00', 178: 'ChiReg_WHI_BleSco_12Yo_BUT_4500ml_01_00', 179: 'ChiReg_WHI_BleSco_12Yo_BUT_500ml_00_00', 180: 'ChiReg_WHI_BleSco_12Yo_BUT_500ml_01_00', 181: 'ChiReg_WHI_BleSco_12Yo_BUT_500ml_02_00', 182: 'ChiReg_WHI_BleSco_12Yo_BUT_50ml_00_00', 183: 'ChiReg_WHI_BleSco_12Yo_BUT_50ml_01_00', 184: 'ChiReg_WHI_BleSco_12Yo_BUT_700ml_00_00', 185: 'ChiReg_WHI_BleSco_12Yo_BUT_700ml_02_00', 186: 'ChiReg_WHI_BleSco_12Yo_KAR_1000ml_15_00', 187: 'ChiReg_WHI_BleSco_12Yo_KAR_1000ml_16_00', 188: 'ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_01_00', 189: 'ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_03_00', 190: 'ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_05_00', 191: 'ChiReg_WHI_BleSco_12Yo_KAR_2SZK_700ml_08_00', 192: 'ChiReg_WHI_BleSco_12Yo_KAR_700ml_00_00', 193: 'ChiReg_WHI_BleSco_12Yo_KAR_700ml_01_00', 194: 'ChiReg_WHI_BleSco_12Yo_KAR_700ml_07_00', 195: 'ChiReg_WHI_BleSco_12Yo_KAR_700ml_08_00', 196: 'ChiReg_WHI_BleSco_12Yo_KAR_700ml_15_00', 197: 'ChiReg_WHI_BleSco_18Yo_BUT_700ml_00_00', 198: 'ChiReg_WHI_BleSco_18Yo_KAR_2SZK_700ml_01_00', 199: 'ChiReg_WHI_BleSco_18Yo_KAR_700ml_01_00', 200: 'ChiReg_WHI_BleSco_18Yo_KAR_700ml_02_00', 201: 'ChiReg_WHI_BleSco_18Yo_KAR_700ml_03_00', 202: 'ChiReg_WHI_BleSco_25Yo_BUT_700ml_00_00', 203: 'ChiReg_WHI_BleSco_25Yo_KAR_700ml_00_00', 204: 'ChiReg_WHI_BleSco_25Yo_KAR_700ml_01_00', 205: 'ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_BUT_700ml_00_00', 206: 'ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_BUT_700ml_02_00', 207: 'ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_KAR_700ml_00_00', 208: 'ChiRegExtraAmeRyeCas_WHI_BleSco_13Yo_KAR_700ml_02_00', 209: 'ChiRegExtraShe_WHI_BleSco_13Yo_BUT_700ml_00_00', 210: 'ChiRegExtraShe_WHI_BleSco_13Yo_BUT_700ml_01_00', 211: 'ChiRegExtraShe_WHI_BleSco_13Yo_KAR_700ml_00_00', 212: 'ChiRegExtraShe_WHI_BleSco_13Yo_KAR_700ml_02_00', 213: 'ChiRegMizun_WHI_BleSco_BUT_700ml_00_00', 214: 'ChiRegMizun_WHI_BleSco_KAR_700ml_00_00', 215: 'ChiRegUltis_WHI_BleSco_BUT_700ml_00_00', 216: 'ChiRegUltis_WHI_BleSco_KAR_700ml_00_00', 217: 'ChiRegXv_WHI_BleSco_15Yo_BUT_700ml_00_00', 218: 'ChiRegXv_WHI_BleSco_15Yo_BUT_700ml_01_00', 219: 'ChiRegXv_WHI_BleSco_15Yo_KAR_700ml_00_00', 220: 'ChiRegXv_WHI_BleSco_15Yo_KAR_700ml_01_00', 221: 'ChiRegXv_WHI_BleSco_15Yo_KAR_700ml_02_00', 222: 'DeKuy_NOT_Rec', 223: 'DeKuyAmare_LIK_BUT_700ml_01_00', 224: 'DeKuyBlueCur_LIK_BUT_500ml_00_00', 225: 'DeKuyBlueCur_LIK_BUT_700ml_00_00', 226: 'DeKuyCremeDeCas_LIK_BUT_700ml_00_00', 227: 'DeKuyTriplSec_LIK_BUT_500ml_00_00', 228: 'DeKuyTriplSec_LIK_BUT_700ml_00_00', 229: 'DeKuyTriplSec_LIK_BUT_700ml_01_00', 230: 'FouRos_NOT_Rec', 231: 'FouRosSinglBar_WHI_Bou_BUT_700ml_00_00', 232: 'FouRosSmallBat_WHI_Bou_BUT_700ml_00_00', 233: 'GleKei_NOT_Rec', 234: 'GleKei_WHI_SinSco_21Yo_BUT_700ml_00_00', 235: 'GleKei_WHI_SinSco_21Yo_KAR_700ml_00_00', 236: 'Gol_NOT_Rec', 237: 'Gol_RUM_BUT_200ml_00_00', 238: 'Gol_RUM_BUT_200ml_01_00', 239: 'Gol_RUM_BUT_500ml_00_00', 240: 'Gol_RUM_BUT_500ml_01_00', 241: 'Hav_NOT_Rec', 242: 'HavClub_RUM_3Yo_BUT_700ml_00_00', 243: 'HavClub_RUM_3Yo_BUT_700ml_01_00', 244: 'HavClub_RUM_7Yo_BUT_700ml_00_00', 245: 'HavClubEsp_RUM_BUT_700ml_00_00', 246: 'HavClubEsp_RUM_BUT_700ml_01_00', 247: 'HavClubSelDeMae_RUM_BUT_700ml_01_00', 248: 'HavClubSelDeMae_RUM_BUT_700ml_02_00', 249: 'HavClubSelDeMae_RUM_KAR_700ml_00_00', 250: 'HavClubSelDeMae_RUM_TUB_700ml_00_00', 251: 'HavClubSelDeMae_RUM_TUB_700ml_01_00', 252: 'JacCre_NOT_Rec', 253: 'JacCreChard_WIN_WytBia_BUT_750ml_00_00', 254: 'JacCreChard_WIN_WytBia_BUT_750ml_01_00', 255: 'JacCreChardPinNoi_WIN_MusWytBia_BUT_750ml_00_00', 256: 'JacCreChardPinNoi_WIN_MusWytBia_BUT_750ml_01_00', 257: 'JacCreChardPinNoi_WIN_MusWytBia_BUT_750ml_02_00', 258: 'JacCreChardRes_WIN_WytBia_BUT_750ml_00_00', 259: 'JacCreChardRes_WIN_WytBia_BUT_750ml_01_00', 260: 'JacCreDoublBarCabSav_WIN_WytCze_BUT_750ml_00_00', 261: 'JacCreDoublBarCabSav_WIN_WytCze_BUT_750ml_01_00', 262: 'JacCreDoublBarShi_WIN_WytCze_BUT_750ml_00_00', 263: 'JacCreDoublBarShi_WIN_WytCze_BUT_750ml_01_00', 264: 'JacCreMerloShi_WIN_PolSloCze_BUT_750ml_00_00', 265: 'JacCreMerloShi_WIN_PolSloCze_BUT_750ml_01_00', 266: 'JacCreMerloShi_WIN_PolSloCze_BUT_750ml_02_00', 267: 'JacCreMoscaRos_WIN_SloRoz_BUT_750ml_00_00', 268: 'JacCreMoscaRos_WIN_SloRoz_BUT_750ml_01_00', 269: 'JacCreMoscaRos_WIN_SloRoz_BUT_750ml_02_00', 270: 'JacCreRiesl_WIN_WytBia_BUT_750ml_00_00', 271: 'JacCreSauviBla_WIN_PolWytBia_BUT_750ml_00_00', 272: 'JacCreSauviBla_WIN_PolWytBia_BUT_750ml_01_00', 273: 'JacCreSauviBla_WIN_PolWytBia_BUT_750ml_02_00', 274: 'JacCreSauviMosTwiPic_WIN_MusWytBia_BUT_750ml_00_00', 275: 'JacCreShiraCab_WIN_WytCze_BUT_750ml_00_00', 276: 'JacCreShiraCab_WIN_WytCze_BUT_750ml_01_00', 277: 'JacCreShiraCab_WIN_WytCze_BUT_750ml_02_00', 278: 'JacCreShiraGre_WIN_PolWytCze_BUT_750ml_00_00', 279: 'JacCreShiraGre_WIN_PolWytCze_BUT_750ml_01_00', 280: 'JacCreShiraGre_WIN_PolWytCze_BUT_750ml_02_00', 281: 'JacCreShiraRes_WIN_WytCze_BUT_750ml_00_00', 282: 'JacCreShiraRes_WIN_WytCze_BUT_750ml_01_00', 283: 'JacCreShiraRos_WIN_PolWytRoz_BUT_750ml_00_00', 284: 'JacCreSparkMos_WIN_MusSloBia_BUT_750ml_01_00', 285: 'JacCreStillMos_WIN_SloBia_BUT_750ml_00_00', 286: 'JacCreStillMos_WIN_SloBia_BUT_750ml_01_00', 287: 'JacCreStillMos_WIN_SloBia_BUT_750ml_02_00', 288: 'JacCreStillMos_WIN_SloBia_BUT_750ml_03_00', 289: 'JacCreStillMos_WIN_SloBia_BUT_750ml_04_00', 290: 'Jam_NOT_Rec', 291: 'Jam_WHI_BleIri_BUT_1000ml_00_00', 292: 'Jam_WHI_BleIri_BUT_200ml_00_00', 293: 'Jam_WHI_BleIri_BUT_200ml_01_00', 294: 'Jam_WHI_BleIri_BUT_500ml_00_00', 295: 'Jam_WHI_BleIri_BUT_700ml_00_00', 296: 'Jam_WHI_BleIri_BUT_700ml_01_00', 297: 'Jam_WHI_BleIri_BUT_STO_4500ml_00_00', 298: 'Jam_WHI_BleIri_KAR_2SZK_700ml_00_00', 299: 'Jam_WHI_BleIri_KAR_MIN_700ml_00_00', 300: 'Jam_WHI_BleIri_KAR_MIN_700ml_01_00', 301: 'Jam_WHI_BleIri_KAR_STO_4500ml_00_00', 302: 'JamBlackBar_WHI_BleIri_BUT_700ml_00_00', 303: 'JamBlackBar_WHI_BleIri_BUT_700ml_01_00', 304: 'JamBlackBar_WHI_BleIri_KAR_700ml_00_00', 305: 'JamBlackBar_WHI_BleIri_KAR_700ml_01_00', 306: 'JamBlendDog_WHI_BleIri_BUT_700ml_00_00', 307: 'JamCaskmIpa_WHI_BleIri_BUT_700ml_00_00', 308: 'JamCaskmIpa_WHI_BleIri_BUT_700ml_01_00', 309: 'JamCaskmIpa_WHI_BleIri_BUT_700ml_02_00', 310: 'JamCaskmIpa_WHI_BleIri_KAR_700ml_00_00', 311: 'JamCaskmIpa_WHI_BleIri_KAR_700ml_01_00', 312: 'JamCaskmSto_WHI_BleIri_BUT_700ml_00_00', 313: 'JamCaskmSto_WHI_BleIri_BUT_700ml_01_00', 314: 'JamCaskmSto_WHI_BleIri_BUT_700ml_02_00', 315: 'JamCaskmSto_WHI_BleIri_BUT_700ml_03_00', 316: 'JamCaskmSto_WHI_BleIri_KAR_700ml_00_00', 317: 'JamCaskmSto_WHI_BleIri_KAR_700ml_01_00', 318: 'JamCoopeCro_WHI_BleIri_BUT_700ml_00_00', 319: 'JamCrest_WHI_BleIri_BUT_700ml_00_00', 320: 'JamCrest_WHI_BleIri_BUT_700ml_01_00', 321: 'JamCrest_WHI_BleIri_KAR_700ml_00_00', 322: 'JamCrest_WHI_BleIri_KAR_700ml_01_00', 323: 'JamDistiSaf_WHI_BleIri_BUT_700ml_00_00', 324: 'JamOrang_WHI_BleIri_BUT_700ml_00_00', 325: 'Kah_LIK_BUT_700ml_00_00', 326: 'Kah_LIK_BUT_700ml_01_00', 327: 'Kah_LIK_BUT_700ml_02_00', 328: 'Kah_NOT_Rec', 329: 'Lil_NOT_Rec', 330: 'LilBlanc_LIK_BUT_750ml_01_00', 331: 'LilBlanc_LIK_BUT_750ml_02_00', 332: 'LilRose_LIK_BUT_750ml_00_00', 333: 'LilRose_LIK_BUT_750ml_01_00', 334: 'Lon_NOT_Rec', 335: 'Lon_WHI_SinSco_16Yo_KAR_700ml_00_00', 336: 'LonTheDisCho_WHI_SinSco_BUT_700ml_00_00', 337: 'LonTheDisCho_WHI_SinSco_KAR_700ml_00_00', 338: 'Luk_NOT_Rec', 339: 'Luk_WOD_Czy_BUT_1000ml_00_00', 340: 'Luk_WOD_Czy_BUT_1000ml_01_00', 341: 'Luk_WOD_Czy_BUT_200ml_00_00', 342: 'Luk_WOD_Czy_BUT_200ml_01_00', 343: 'Luk_WOD_Czy_BUT_500ml_00_00', 344: 'Luk_WOD_Czy_BUT_500ml_01_00', 345: 'Luk_WOD_Czy_BUT_700ml_00_00', 346: 'Luk_WOD_Czy_BUT_700ml_01_00', 347: 'LukWisni_WOD_Sma_BUT_500ml_00_00', 348: 'Mal_LIK_BUT_500ml_00_00', 349: 'Mal_LIK_BUT_700ml_00_00', 350: 'Mal_LIK_BUT_700ml_01_00', 351: 'Mal_LIK_BUT_700ml_02_00', 352: 'Mal_NOT_Rec', 353: 'Malf_NOT_Rec', 354: 'MalfConAra_GIN_BUT_700ml_00_00', 355: 'MalfConLim_GIN_BUT_700ml_00_00', 356: 'MalfOrigiIta_GIN_BUT_700ml_00_00', 357: 'MalfRosaIta_GIN_BUT_700ml_00_00', 358: 'MalLime_LIK_BUT_700ml_00_00', 359: 'MalPassiFru_LIK_BUT_700ml_00_00', 360: 'MalPassiFru_LIK_BUT_700ml_01_00', 361: 'MalPassiFru_LIK_BUT_700ml_02_00', 362: 'Mar_NOT_Rec', 363: 'MarBlueSwi_KON_BUT_700ml_00_00', 364: 'MarBlueSwi_KON_KAR_700ml_00_00', 365: 'MarCordoBle_KON_BUT_700ml_00_00', 366: 'MarCordoBle_KON_KAR_700ml_00_00', 367: 'MarLor_KON_BUT_700ml_00_00', 368: 'MarLor_KON_TUB_700ml_00_00', 369: 'MarMedai_KON_BUT_700ml_00_00', 370: 'MarMedai_KON_BUT_700ml_02_00', 371: 'MarMedai_KON_BUT_700ml_03_00', 372: 'MarMedai_KON_KAR_700ml_00_00', 373: 'MarMedai_KON_KAR_700ml_02_00', 374: 'MarMedai_KON_KAR_700ml_03_00', 375: 'MarMedai_KON_KAR_700ml_04_00', 376: 'MarMedai_KON_KAR_700ml_05_00', 377: 'MarMedai_KON_KAR_700ml_06_00', 378: 'MarVs_KON_BUT_700ml_00_00', 379: 'MarVs_KON_BUT_700ml_01_00', 380: 'MarVs_KON_KAR_2KIE_700ml_01_00', 381: 'MarVs_KON_KAR_700ml_00_00', 382: 'MarVs_KON_KAR_700ml_01_00', 383: 'MarVsopRedBar_KON_KAR_2SZK_700ml_00_00', 384: 'MarVsopRedBar_KON_KAR_2SZK_700ml_01_00', 385: 'MarVsopRedBar_KON_KAR_2SZK_700ml_02_00', 386: 'MarVsSinDis_KON_BUT_700ml_00_00', 387: 'MarVsSinDis_KON_KAR_700ml_00_00', 388: 'MarVsSinDis_KON_KAR_700ml_01_00', 389: 'MarVsSinDis_KON_KAR_700ml_02_00', 390: 'MarVsSinDis_KON_KAR_KIE_700ml_00_00', 391: 'MarXo_KON_BUT_700ml_00_00', 392: 'MarXo_KON_BUT_700ml_01_00', 393: 'MarXo_KON_KAR_700ml_00_00', 394: 'Mon47_NOT_Rec', 395: 'Mon47Dry_GIN_BUT_500ml_00_00', 396: 'Mon47Sloe_GIN_BUT_500ml_00_00', 397: 'Mum_NOT_Rec', 398: 'MumDemiSec_SZA_BUT_750ml_00_00', 399: 'MumDemiSec_SZA_BUT_750ml_01_00', 400: 'MumGrandCor_SZA_BUT_750ml_00_00', 401: 'MumGrandCor_SZA_KAR_2KIE_750ml_00_00', 402: 'MumGrandCor_SZA_KAR_750ml_00_00', 403: 'MumGrandCorRos_SZA_BUT_750ml_00_00', 404: 'MumIceExt_SZA_BUT_750ml_00_00', 405: 'MumOlymp_SZA_BUT_750ml_01_00', 406: 'MumRose_SZA_BUT_750ml_00_00', 407: 'MumRose_SZA_BUT_750ml_01_00', 408: 'MumRouge_SZA_BUT_1500ml_00_00', 409: 'MumRouge_SZA_BUT_3000ml_00_00', 410: 'MumRouge_SZA_BUT_375ml_00_00', 411: 'MumRouge_SZA_BUT_375ml_01_00', 412: 'MumRouge_SZA_BUT_750ml_00_00', 413: 'MumRouge_SZA_BUT_750ml_01_00', 414: 'MumRouge_SZA_KAR_750ml_00_00', 415: 'MumRouge_SZA_KAR_750ml_02_00', 416: 'MumRouge_SZA_KAR_750ml_03_00', 417: 'MumRouge_SZA_KAR_750ml_05_00', 418: 'MumRouge_SZA_KAR_750ml_06_00', 419: 'MumRouge_SZA_SKR_3000ml_00_00', 420: 'Olm_NOT_Rec', 421: 'OlmAltosPla_TEQ_BUT_700ml_00_00', 422: 'OlmAltosRep_TEQ_BUT_700ml_00_00', 423: 'OlmBlanc_TEQ_BUT_700ml_00_00', 424: 'OlmBlanc_TEQ_BUT_700ml_01_00', 425: 'OlmBlanc_TEQ_BUT_700ml_02_00', 426: 'OlmGold_TEQ_BUT_700ml_00_00', 427: 'OlmGold_TEQ_BUT_700ml_01_00', 428: 'OlmGold_TEQ_BUT_700ml_02_00', 429: 'Ost_NOT_Rec', 430: 'Ost_WOD_Czy_BUT_1000ml_00_00', 431: 'Ost_WOD_Czy_BUT_1750ml_00_00', 432: 'Ost_WOD_Czy_BUT_3000ml_00_00', 433: 'Ost_WOD_Czy_BUT_500ml_00_00', 434: 'Ost_WOD_Czy_BUT_700ml_00_00', 435: 'Ost_WOD_Czy_KAR_700ml_00_00', 436: 'Ost_WOD_Czy_KAR_KIEMIO_700ml_00_00', 437: 'OstBlack_WOD_Czy_BUT_700ml_00_00', 438: 'OstVap_WOD_Czy_KAR_700ml_00_00', 439: 'PanTad_NOT_Rec', 440: 'PanTad_WOD_Czy_BUT_500ml_00_00', 441: 'PanTad_WOD_Czy_BUT_500ml_01_00', 442: 'PanTad_WOD_Czy_BUT_500ml_02_00', 443: 'PanTad_WOD_Czy_BUT_500ml_03_00', 444: 'PanTad_WOD_Czy_BUT_500ml_05_00', 445: 'PanTad_WOD_Czy_BUT_700ml_00_00', 446: 'PanTad_WOD_Czy_BUT_700ml_01_00', 447: 'PanTad_WOD_Czy_BUT_700ml_02_00', 448: 'PanTad_WOD_Czy_BUT_700ml_03_00', 449: 'PanTad_WOD_Czy_BUT_700ml_06_00', 450: 'PanTadAroni_WOD_Sma_BUT_500ml_00_00', 451: 'PanTadAroni_WOD_Sma_BUT_500ml_01_00', 452: 'PanTadAroni_WOD_Sma_BUT_500ml_02_00', 453: 'PanTadAroni_WOD_Sma_BUT_500ml_03_00', 454: 'PanTadPrzep_WOD_Sma_BUT_500ml_00_00', 455: 'PanTadPrzep_WOD_Sma_BUT_500ml_01_00', 456: 'PanTadPrzep_WOD_Sma_BUT_500ml_02_00', 457: 'PanTadPrzep_WOD_Sma_BUT_500ml_03_00', 458: 'PanZosSlonyKar_LIK_BUT_500ml_00_00', 459: 'Pas_NOT_Rec', 460: 'Pas_WHI_BleSco_BUT_1000ml_00_00', 461: 'Pas_WHI_BleSco_BUT_1000ml_01_00', 462: 'Pas_WHI_BleSco_BUT_200ml_00_00', 463: 'Pas_WHI_BleSco_BUT_500ml_00_00', 464: 'Pas_WHI_BleSco_BUT_500ml_01_00', 465: 'Pas_WHI_BleSco_BUT_700ml_00_00', 466: 'Pas_WHI_BleSco_BUT_700ml_01_00', 467: 'PerJou_NOT_Rec', 468: 'PerJouBelleEpo_SZA_BUT_750ml_00_00', 469: 'PerJouBelleEpo_SZA_BUT_750ml_01_00', 470: 'PerJouBelleEpo_SZA_KAR_750ml_00_00', 471: 'PerJouGrandBru_SZA_BUT_750ml_00_00', 472: 'PerJouGrandBru_SZA_BUT_750ml_01_00', 473: 'PerJouGrandBru_SZA_KAR_750ml_01_00', 474: 'Pols_NOT_Rec', 475: 'PolsWisni_WOD_Sma_BUT_200ml_00_00', 476: 'PolsWisni_WOD_Sma_BUT_500ml_00_00', 477: 'Red_NOT_Rec', 478: 'Red_WHI_SinIri_12Yo_BUT_700ml_00_00', 479: 'Red_WHI_SinIri_12Yo_BUT_700ml_01_00', 480: 'Red_WHI_SinIri_12Yo_KAR_700ml_00_00', 481: 'Red_WHI_SinIri_12Yo_KAR_700ml_01_00', 482: 'Red_WHI_SinIri_21Yo_BUT_700ml_00_00', 483: 'Red_WHI_SinIri_21Yo_KAR_700ml_00_00', 484: 'Ric_LIK_BUT_700ml_00_00', 485: 'Ric_LIK_BUT_700ml_01_00', 486: 'Ric_NOT_Rec', 487: 'Robocza', 488: 'RoySal_NOT_Rec', 489: 'RoySal_WHI_BleSco_21Yo_BUT_700ml_00_00', 490: 'RoySal_WHI_BleSco_21Yo_BUT_700ml_01_00', 491: 'RoySal_WHI_BleSco_21Yo_KAR_700ml_00_00', 492: 'RoySal_WHI_BleSco_21Yo_KAR_700ml_01_00', 493: 'RoySalDiamoTri_WHI_BleSco_BUT_700ml_00_00', 494: 'RoySalDiamoTri_WHI_BleSco_KAR_700ml_00_00', 495: 'Sca_NOT_Rec', 496: 'ScaGlans_WHI_SinSco_BUT_700ml_00_00', 497: 'ScaGlans_WHI_SinSco_KAR_700ml_00_00', 498: 'ScaSkire_WHI_SinSco_BUT_700ml_00_00', 499: 'ScaSkire_WHI_SinSco_KAR_700ml_00_00', 500: 'SeaGin_GIN_BUT_350ml_01_00', 501: 'SeaGin_GIN_BUT_700ml_01_00', 502: 'SeaGin_NOT_Rec', 503: 'SeaGinLime_GIN_BUT_350ml_01_00', 504: 'SeaGinLime_GIN_BUT_700ml_01_00', 505: 'Siw_NOT_Rec', 506: 'Siw_WOD_Sma_BUT_500ml_00_00', 507: 'Siw_WOD_Sma_BUT_500ml_01_00', 508: 'TheGle_NOT_Rec', 509: 'TheGle_WHI_SinSco_12Yo_BUT_50ml_00_00', 510: 'TheGle_WHI_SinSco_12Yo_BUT_700ml_00_00', 511: 'TheGle_WHI_SinSco_12Yo_BUT_700ml_01_00', 512: 'TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_00_00', 513: 'TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_01_00', 514: 'TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_02_00', 515: 'TheGle_WHI_SinSco_12Yo_KAR_2SZK_700ml_03_00', 516: 'TheGle_WHI_SinSco_12Yo_KAR_700ml_00_00', 517: 'TheGle_WHI_SinSco_12Yo_KAR_700ml_01_00', 518: 'TheGle_WHI_SinSco_12Yo_KAR_SKA_700ml_00_00', 519: 'TheGle_WHI_SinSco_15Yo_BUT_700ml_00_00', 520: 'TheGle_WHI_SinSco_15Yo_BUT_700ml_01_00', 521: 'TheGle_WHI_SinSco_15Yo_KAR_2SZK_700ml_00_00', 522: 'TheGle_WHI_SinSco_15Yo_KAR_700ml_00_00', 523: 'TheGle_WHI_SinSco_15Yo_KAR_700ml_01_00', 524: 'TheGle_WHI_SinSco_18Yo_BUT_700ml_00_00', 525: 'TheGle_WHI_SinSco_18Yo_BUT_700ml_01_00', 526: 'TheGle_WHI_SinSco_18Yo_KAR_700ml_00_00', 527: 'TheGle_WHI_SinSco_18Yo_KAR_700ml_01_00', 528: 'TheGle_WHI_SinSco_21Yo_BUT_700ml_00_00', 529: 'TheGle_WHI_SinSco_21Yo_KAR_700ml_00_00', 530: 'TheGle_WHI_SinSco_25Yo_BUT_700ml_00_00', 531: 'TheGle_WHI_SinSco_25Yo_SKR_700ml_00_00', 532: 'TheGleFoundRes_WHI_SinSco_BUT_700ml_00_00', 533: 'TheGleFoundRes_WHI_SinSco_BUT_700ml_01_00', 534: 'TheGleFoundRes_WHI_SinSco_KAR_700ml_00_00', 535: 'TheGleFoundRes_WHI_SinSco_KAR_700ml_01_00', 536: 'TheGleIllicSti_WHI_SinSco_12Yo_BUT_700ml_00_00', 537: 'TheGleLicenDra_WHI_SinSco_12Yo_BUT_700ml_00_00', 538: 'TheGleLicenDra_WHI_SinSco_12Yo_KAR_700ml_00_00', 539: 'TheGleSinglCasSheBut_WHI_SinSco_14Yo_BUT_700ml_00_00', 540: 'TheGleSinglCasSheBut_WHI_SinSco_14Yo_KAR_700ml_00_00', 541: 'TheGleSpect_WHI_SinSco_KAR_600ml_00_00', 542: 'Wyb_NOT_Rec', 543: 'Wyb_WOD_Czy_BUT_1000ml_00_00', 544: 'Wyb_WOD_Czy_BUT_200ml_00_00', 545: 'Wyb_WOD_Czy_BUT_500ml_00_00', 546: 'Wyb_WOD_Czy_BUT_50ml_00_00', 547: 'Wyb_WOD_Czy_BUT_700ml_00_00', 548: 'Wyb_WOD_Czy_BUT_700ml_01_00', 549: 'WybAgres_WOD_Sma_BUT_200ml_00_00', 550: 'WybAgres_WOD_Sma_BUT_500ml_00_00', 551: 'WybCzarnPor_WOD_Sma_BUT_200ml_00_00', 552: 'WybCzarnPor_WOD_Sma_BUT_500ml_00_00', 553: 'WybCzarnPor_WOD_Sma_BUT_500ml_01_00', 554: 'WybCzarnPor_WOD_Sma_BUT_500ml_02_00', 555: 'WybExqui_WOD_Czy_BUT_1750ml_00_00', 556: 'WybExqui_WOD_Czy_BUT_700ml_00_00', 557: 'WybExqui_WOD_Czy_KAR_700ml_00_00', 558: 'WybGrusz_WOD_Sma_BUT_200ml_00_00', 559: 'WybGrusz_WOD_Sma_BUT_500ml_00_00', 560: 'WybGrusz_WOD_Sma_BUT_500ml_01_00', 561: 'WybGrusz_WOD_Sma_BUT_500ml_02_00', 562: 'WybOdMis_WOD_Czy_BUT_500ml_00_00', 563: 'WybOdMis_WOD_Czy_BUT_700ml_00_00', 564: 'WybProst_WOD_Czy_BUT_500ml_00_00', 565: 'WybProst_WOD_Czy_BUT_500ml_01_00', 566: 'WybProst_WOD_Czy_BUT_500ml_02_00', 567: 'WybProst_WOD_Czy_BUT_700ml_00_00', 568: 'WybProst_WOD_Czy_BUT_700ml_01_00', 569: 'WybProst_WOD_Czy_BUT_700ml_02_00', 570: 'WybPszen_WOD_Czy_BUT_500ml_00_00', 571: 'WybPszen_WOD_Czy_BUT_500ml_01_00', 572: 'WybPszen_WOD_Czy_BUT_500ml_02_00', 573: 'WybSliwk_WOD_Sma_BUT_200ml_00_00', 574: 'WybSliwk_WOD_Sma_BUT_200ml_01_00', 575: 'WybSliwk_WOD_Sma_BUT_500ml_00_00', 576: 'WybSliwk_WOD_Sma_BUT_500ml_01_00', 577: 'WybSliwk_WOD_Sma_BUT_500ml_02_00', 578: 'WybWisni_WOD_Sma_BUT_200ml_00_00', 579: 'WybWisni_WOD_Sma_BUT_200ml_01_00', 580: 'WybWisni_WOD_Sma_BUT_500ml_00_00', 581: 'WybWisni_WOD_Sma_BUT_500ml_01_00', 582: 'WybWisni_WOD_Sma_BUT_500ml_02_00', 583: 'WybZiemn_WOD_Czy_BUT_500ml_00_00', 584: 'WybZiemn_WOD_Czy_BUT_500ml_01_00', 585: 'WybZiemn_WOD_Czy_BUT_500ml_02_00'}, 'max_instances_per_image': 100, 'regenerate_source_id': False, 'min_level': 3, 'max_level': 7, 'num_scales': 3, 'aspect_ratios': [1.0, 2.0, 0.5], 'anchor_scale': 4.0, 'is_training_bn': True, 'momentum': 0.9, 'optimizer': 'sgd', 'learning_rate': 0.8, 'lr_warmup_init': 0.08, 'lr_warmup_epoch': 1.0, 'first_lr_drop_epoch': 200.0, 'second_lr_drop_epoch': 250.0, 'poly_lr_power': 0.9, 'clip_gradients_norm': 10.0, 'num_epochs': 1, 'data_format': 'channels_last', 'mean_rgb': [123.675, 116.28, 103.53], 'stddev_rgb': [58.395, 57.120000000000005, 57.375], 'scale_range': False, 'label_smoothing': 0.0, 'alpha': 0.25, 'gamma': 1.5, 'delta': 0.1, 'box_loss_weight': 50.0, 'iou_loss_type': None, 'iou_loss_weight': 1.0, 'weight_decay': 4e-05, 'strategy': None, 'mixed_precision': False, 'loss_scale': None, 'box_class_repeats': 4, 'fpn_cell_repeats': 6, 'fpn_num_filters': 160, 'separable_conv': True, 'apply_bn_for_resampling': True, 'conv_after_downsample': False, 'conv_bn_act_pattern': False, 'drop_remainder': True, 'nms_configs': {'method': 'gaussian', 'iou_thresh': None, 'score_thresh': 0.0, 'sigma': None, 'pyfunc': False, 'max_nms_inputs': 0, 'max_output_size': 100}, 'tflite_max_detections': 100, 'fpn_name': None, 'fpn_weight_method': None, 'fpn_config': None, 'survival_prob': None, 'img_summary_steps': None, 'lr_decay_method': 'cosine', 'moving_average_decay': 0.9998, 'ckpt_var_scope': None, 'skip_mismatch': True, 'backbone_name': 'efficientnet-b3', 'backbone_config': None, 'var_freeze_expr': '(efficientnet|fpn_cells|resample_p6)', 'use_keras_model': True, 'dataset_type': None, 'positives_momentum': None, 'grad_checkpoint': False, 'verbose': 1, 'save_freq': 'epoch', 'model_name': 'efficientdet-d3', 'iterations_per_loop': 1000, 'model_dir': './tmp/efficientdet-d3-finetune', 'num_shards': 8, 'num_examples_per_epoch': 125, 'backbone_ckpt': '', 'ckpt': 'efficientdet-d3', 'val_json_file': None, 'testdev_dir': None, 'profile': False, 'mode': 'train'}
INFO:tensorflow:Using config: {'_model_dir': './tmp/efficientdet-d3-finetune', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 1000, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
I0102 23:06:55.367058 140562995341120 estimator.py:202] Using config: {'_model_dir': './tmp/efficientdet-d3-finetune', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 1000, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': './tmp/efficientdet-d3-finetune', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 1000, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
I0102 23:06:55.368503 140562995341120 estimator.py:202] Using config: {'_model_dir': './tmp/efficientdet-d3-finetune', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 1000, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /home/da/py38/lib/python3.8/site-packages/tensorflow/python/training/training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0102 23:06:55.376179 140562995341120 deprecation.py:337] From /home/da/py38/lib/python3.8/site-packages/tensorflow/python/training/training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
I0102 23:06:55.816170 140562995341120 dataloader.py:84] target_size = (896, 896), output_size = (896, 896)
INFO:tensorflow:Calling model_fn.
I0102 23:06:56.385631 140562995341120 estimator.py:1173] Calling model_fn.
I0102 23:06:56.385888 140562995341120 utils.py:535] use mixed precision policy name float32
WARNING:tensorflow:From /home/da/git/woj/efficientdet/utils.py:536: The name tf.keras.layers.enable_v2_dtype_behavior is deprecated. Please use tf.compat.v1.keras.layers.enable_v2_dtype_behavior instead.

W0102 23:06:56.663122 140562995341120 module_wrapper.py:149] From /home/da/git/woj/efficientdet/utils.py:536: The name tf.keras.layers.enable_v2_dtype_behavior is deprecated. Please use tf.compat.v1.keras.layers.enable_v2_dtype_behavior instead.

I0102 23:06:56.668133 140562995341120 efficientnet_builder.py:215] global_params= GlobalParams(batch_norm_momentum=0.99, batch_norm_epsilon=0.001, dropout_rate=0.3, data_format='channels_last', num_classes=1000, width_coefficient=1.2, depth_coefficient=1.4, depth_divisor=8, min_depth=None, survival_prob=0.8, relu_fn=functools.partial(<function activation_fn at 0x7fd68a79b670>, act_type='swish'), batch_norm=<class 'utils.BatchNormalization'>, use_se=True, local_pooling=None, condconv_num_experts=None, clip_projection_output=False, blocks_args=['r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', 'r1_k3_s11_e6_i192_o320_se0.25'], fix_head_stem=None, grad_checkpoint=False)
I0102 23:06:57.114061 140562995341120 efficientdet_keras.py:750] building FPNCell cell_0
I0102 23:06:57.114546 140562995341120 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}
I0102 23:06:57.115497 140562995341120 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}
I0102 23:06:57.116436 140562995341120 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}
I0102 23:06:57.117397 140562995341120 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}
I0102 23:06:57.119197 140562995341120 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}
I0102 23:06:57.120199 140562995341120 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}
I0102 23:06:57.121195 140562995341120 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}
I0102 23:06:57.122211 140562995341120 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}
I0102 23:06:57.123452 140562995341120 efficientdet_keras.py:750] building FPNCell cell_1
I0102 23:06:57.123817 140562995341120 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}
I0102 23:06:57.124748 140562995341120 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}
I0102 23:06:57.125736 140562995341120 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}
I0102 23:06:57.126750 140562995341120 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}
I0102 23:06:57.127735 140562995341120 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}
I0102 23:06:57.128686 140562995341120 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}
I0102 23:06:57.129684 140562995341120 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}
I0102 23:06:57.130661 140562995341120 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}
I0102 23:06:57.131980 140562995341120 efficientdet_keras.py:750] building FPNCell cell_2
I0102 23:06:57.132383 140562995341120 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}
I0102 23:06:57.133302 140562995341120 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}
I0102 23:06:57.134217 140562995341120 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}
I0102 23:06:57.135276 140562995341120 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}
I0102 23:06:57.136222 140562995341120 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}
I0102 23:06:57.137199 140562995341120 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}
I0102 23:06:57.138221 140562995341120 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}
I0102 23:06:57.139251 140562995341120 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}
I0102 23:06:57.140518 140562995341120 efficientdet_keras.py:750] building FPNCell cell_3
I0102 23:06:57.140926 140562995341120 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}
I0102 23:06:57.141898 140562995341120 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}
I0102 23:06:57.142953 140562995341120 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}
I0102 23:06:57.143951 140562995341120 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}
I0102 23:06:57.144974 140562995341120 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}
I0102 23:06:57.145984 140562995341120 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}
I0102 23:06:57.147073 140562995341120 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}
I0102 23:06:57.148193 140562995341120 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}
I0102 23:06:57.149541 140562995341120 efficientdet_keras.py:750] building FPNCell cell_4
I0102 23:06:57.149949 140562995341120 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}
I0102 23:06:57.151009 140562995341120 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}
I0102 23:06:57.152015 140562995341120 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}
I0102 23:06:57.152967 140562995341120 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}
I0102 23:06:57.154082 140562995341120 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}
I0102 23:06:57.155227 140562995341120 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}
I0102 23:06:57.156267 140562995341120 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}
I0102 23:06:57.157230 140562995341120 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}
I0102 23:06:57.158569 140562995341120 efficientdet_keras.py:750] building FPNCell cell_5
I0102 23:06:57.158961 140562995341120 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}
I0102 23:06:57.159890 140562995341120 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}
I0102 23:06:57.160845 140562995341120 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}
I0102 23:06:57.161807 140562995341120 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}
I0102 23:06:57.162744 140562995341120 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}
I0102 23:06:57.163748 140562995341120 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}
I0102 23:06:57.164839 140562995341120 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}
I0102 23:06:57.165806 140562995341120 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}
WARNING:tensorflow:From /home/da/py38/lib/python3.8/site-packages/keras/layers/normalization/batch_normalization.py:532: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
W0102 23:06:57.293271 140562995341120 deprecation.py:337] From /home/da/py38/lib/python3.8/site-packages/keras/layers/normalization/batch_normalization.py:532: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
I0102 23:06:57.308187 140562995341120 efficientnet_model.py:734] Built stem stem : (2, 448, 448, 40)
I0102 23:06:57.308448 140562995341120 efficientnet_model.py:755] block_0 survival_prob: 1.0
I0102 23:06:57.308863 140562995341120 efficientnet_model.py:373] Block blocks_0 input shape: (2, 448, 448, 40)
I0102 23:06:57.351354 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 448, 448, 40)
I0102 23:06:57.385758 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 40)
I0102 23:06:57.426656 140562995341120 efficientnet_model.py:413] Project shape: (2, 448, 448, 24)
I0102 23:06:57.427096 140562995341120 efficientnet_model.py:755] block_1 survival_prob: 0.9923076923076923
I0102 23:06:57.427569 140562995341120 efficientnet_model.py:373] Block blocks_1 input shape: (2, 448, 448, 24)
I0102 23:06:57.470309 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 448, 448, 24)
I0102 23:06:57.503290 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 24)
I0102 23:06:57.554880 140562995341120 efficientnet_model.py:413] Project shape: (2, 448, 448, 24)
I0102 23:06:57.555342 140562995341120 efficientnet_model.py:755] block_2 survival_prob: 0.9846153846153847
I0102 23:06:57.555924 140562995341120 efficientnet_model.py:373] Block blocks_2 input shape: (2, 448, 448, 24)
I0102 23:06:57.599646 140562995341120 efficientnet_model.py:389] Expand shape: (2, 448, 448, 144)
I0102 23:06:57.643708 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 224, 224, 144)
I0102 23:06:57.677206 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 144)
I0102 23:06:57.717100 140562995341120 efficientnet_model.py:413] Project shape: (2, 224, 224, 32)
I0102 23:06:57.717492 140562995341120 efficientnet_model.py:755] block_3 survival_prob: 0.9769230769230769
I0102 23:06:57.717926 140562995341120 efficientnet_model.py:373] Block blocks_3 input shape: (2, 224, 224, 32)
I0102 23:06:57.761598 140562995341120 efficientnet_model.py:389] Expand shape: (2, 224, 224, 192)
I0102 23:06:57.807198 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 224, 224, 192)
I0102 23:06:57.843380 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 192)
I0102 23:06:57.894041 140562995341120 efficientnet_model.py:413] Project shape: (2, 224, 224, 32)
I0102 23:06:57.894551 140562995341120 efficientnet_model.py:755] block_4 survival_prob: 0.9692307692307692
I0102 23:06:57.895071 140562995341120 efficientnet_model.py:373] Block blocks_4 input shape: (2, 224, 224, 32)
I0102 23:06:57.939154 140562995341120 efficientnet_model.py:389] Expand shape: (2, 224, 224, 192)
I0102 23:06:57.989865 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 224, 224, 192)
I0102 23:06:58.026333 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 192)
I0102 23:06:58.083609 140562995341120 efficientnet_model.py:413] Project shape: (2, 224, 224, 32)
I0102 23:06:58.084105 140562995341120 efficientnet_model.py:755] block_5 survival_prob: 0.9615384615384616
I0102 23:06:58.084609 140562995341120 efficientnet_model.py:373] Block blocks_5 input shape: (2, 224, 224, 32)
I0102 23:06:58.132211 140562995341120 efficientnet_model.py:389] Expand shape: (2, 224, 224, 192)
I0102 23:06:58.177023 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 112, 112, 192)
I0102 23:06:58.210987 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 192)
I0102 23:06:58.253363 140562995341120 efficientnet_model.py:413] Project shape: (2, 112, 112, 48)
I0102 23:06:58.253793 140562995341120 efficientnet_model.py:755] block_6 survival_prob: 0.9538461538461539
I0102 23:06:58.254362 140562995341120 efficientnet_model.py:373] Block blocks_6 input shape: (2, 112, 112, 48)
I0102 23:06:58.299015 140562995341120 efficientnet_model.py:389] Expand shape: (2, 112, 112, 288)
I0102 23:06:58.343600 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 112, 112, 288)
I0102 23:06:58.378880 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 288)
I0102 23:06:58.431383 140562995341120 efficientnet_model.py:413] Project shape: (2, 112, 112, 48)
I0102 23:06:58.431832 140562995341120 efficientnet_model.py:755] block_7 survival_prob: 0.9461538461538461
I0102 23:06:58.432401 140562995341120 efficientnet_model.py:373] Block blocks_7 input shape: (2, 112, 112, 48)
I0102 23:06:58.477589 140562995341120 efficientnet_model.py:389] Expand shape: (2, 112, 112, 288)
I0102 23:06:58.529271 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 112, 112, 288)
I0102 23:06:58.564348 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 288)
I0102 23:06:58.615775 140562995341120 efficientnet_model.py:413] Project shape: (2, 112, 112, 48)
I0102 23:06:58.616335 140562995341120 efficientnet_model.py:755] block_8 survival_prob: 0.9384615384615385
I0102 23:06:58.616868 140562995341120 efficientnet_model.py:373] Block blocks_8 input shape: (2, 112, 112, 48)
I0102 23:06:58.665363 140562995341120 efficientnet_model.py:389] Expand shape: (2, 112, 112, 288)
I0102 23:06:58.711553 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 288)
I0102 23:06:58.750468 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 288)
I0102 23:06:58.791332 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 96)
I0102 23:06:58.791729 140562995341120 efficientnet_model.py:755] block_9 survival_prob: 0.9307692307692308
I0102 23:06:58.792159 140562995341120 efficientnet_model.py:373] Block blocks_9 input shape: (2, 56, 56, 96)
I0102 23:06:58.835121 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 576)
I0102 23:06:59.033549 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 576)
I0102 23:06:59.068808 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 576)
I0102 23:06:59.118613 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 96)
I0102 23:06:59.119120 140562995341120 efficientnet_model.py:755] block_10 survival_prob: 0.9230769230769231
I0102 23:06:59.119661 140562995341120 efficientnet_model.py:373] Block blocks_10 input shape: (2, 56, 56, 96)
I0102 23:06:59.162073 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 576)
I0102 23:06:59.207022 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 576)
I0102 23:06:59.243650 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 576)
I0102 23:06:59.296999 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 96)
I0102 23:06:59.297444 140562995341120 efficientnet_model.py:755] block_11 survival_prob: 0.9153846153846155
I0102 23:06:59.297930 140562995341120 efficientnet_model.py:373] Block blocks_11 input shape: (2, 56, 56, 96)
I0102 23:06:59.342265 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 576)
I0102 23:06:59.388134 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 576)
I0102 23:06:59.422876 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 576)
I0102 23:06:59.474277 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 96)
I0102 23:06:59.474753 140562995341120 efficientnet_model.py:755] block_12 survival_prob: 0.9076923076923077
I0102 23:06:59.475201 140562995341120 efficientnet_model.py:373] Block blocks_12 input shape: (2, 56, 56, 96)
I0102 23:06:59.518475 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 576)
I0102 23:06:59.564408 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 576)
I0102 23:06:59.599868 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 576)
I0102 23:06:59.652817 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 96)
I0102 23:06:59.653294 140562995341120 efficientnet_model.py:755] block_13 survival_prob: 0.9
I0102 23:06:59.653870 140562995341120 efficientnet_model.py:373] Block blocks_13 input shape: (2, 56, 56, 96)
I0102 23:06:59.697269 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 576)
I0102 23:06:59.742204 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 576)
I0102 23:06:59.778071 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 576)
I0102 23:06:59.822216 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 136)
I0102 23:06:59.822714 140562995341120 efficientnet_model.py:755] block_14 survival_prob: 0.8923076923076924
I0102 23:06:59.823220 140562995341120 efficientnet_model.py:373] Block blocks_14 input shape: (2, 56, 56, 136)
I0102 23:06:59.871847 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 816)
I0102 23:06:59.917706 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 816)
I0102 23:06:59.953016 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 816)
I0102 23:07:00.005156 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 136)
I0102 23:07:00.005595 140562995341120 efficientnet_model.py:755] block_15 survival_prob: 0.8846153846153847
I0102 23:07:00.006058 140562995341120 efficientnet_model.py:373] Block blocks_15 input shape: (2, 56, 56, 136)
I0102 23:07:00.052236 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 816)
I0102 23:07:00.098638 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 816)
I0102 23:07:00.134895 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 816)
I0102 23:07:00.185768 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 136)
I0102 23:07:00.186198 140562995341120 efficientnet_model.py:755] block_16 survival_prob: 0.8769230769230769
I0102 23:07:00.186750 140562995341120 efficientnet_model.py:373] Block blocks_16 input shape: (2, 56, 56, 136)
I0102 23:07:00.238000 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 816)
I0102 23:07:00.283428 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 816)
I0102 23:07:00.319485 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 816)
I0102 23:07:00.372308 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 136)
I0102 23:07:00.372743 140562995341120 efficientnet_model.py:755] block_17 survival_prob: 0.8692307692307693
I0102 23:07:00.373246 140562995341120 efficientnet_model.py:373] Block blocks_17 input shape: (2, 56, 56, 136)
I0102 23:07:00.418221 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 816)
I0102 23:07:00.464493 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 56, 56, 816)
I0102 23:07:00.500797 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 816)
I0102 23:07:00.552797 140562995341120 efficientnet_model.py:413] Project shape: (2, 56, 56, 136)
I0102 23:07:00.553343 140562995341120 efficientnet_model.py:755] block_18 survival_prob: 0.8615384615384616
I0102 23:07:00.553816 140562995341120 efficientnet_model.py:373] Block blocks_18 input shape: (2, 56, 56, 136)
I0102 23:07:00.598924 140562995341120 efficientnet_model.py:389] Expand shape: (2, 56, 56, 816)
I0102 23:07:00.645026 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 816)
I0102 23:07:00.681046 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 816)
I0102 23:07:00.722500 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 232)
I0102 23:07:00.722957 140562995341120 efficientnet_model.py:755] block_19 survival_prob: 0.8538461538461539
I0102 23:07:00.723477 140562995341120 efficientnet_model.py:373] Block blocks_19 input shape: (2, 28, 28, 232)
I0102 23:07:00.774166 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 1392)
I0102 23:07:00.826123 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 1392)
I0102 23:07:00.863637 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 1392)
I0102 23:07:00.914356 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 232)
I0102 23:07:00.914877 140562995341120 efficientnet_model.py:755] block_20 survival_prob: 0.8461538461538463
I0102 23:07:00.915332 140562995341120 efficientnet_model.py:373] Block blocks_20 input shape: (2, 28, 28, 232)
I0102 23:07:00.965972 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 1392)
I0102 23:07:01.018671 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 1392)
I0102 23:07:01.055501 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 1392)
I0102 23:07:01.105556 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 232)
I0102 23:07:01.105966 140562995341120 efficientnet_model.py:755] block_21 survival_prob: 0.8384615384615385
I0102 23:07:01.106441 140562995341120 efficientnet_model.py:373] Block blocks_21 input shape: (2, 28, 28, 232)
I0102 23:07:01.156986 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 1392)
I0102 23:07:01.206625 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 1392)
I0102 23:07:01.243858 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 1392)
I0102 23:07:01.295665 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 232)
I0102 23:07:01.296208 140562995341120 efficientnet_model.py:755] block_22 survival_prob: 0.8307692307692308
I0102 23:07:01.296733 140562995341120 efficientnet_model.py:373] Block blocks_22 input shape: (2, 28, 28, 232)
I0102 23:07:01.348539 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 1392)
I0102 23:07:01.400004 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 1392)
I0102 23:07:01.436683 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 1392)
I0102 23:07:01.486699 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 232)
I0102 23:07:01.487220 140562995341120 efficientnet_model.py:755] block_23 survival_prob: 0.8230769230769232
I0102 23:07:01.487727 140562995341120 efficientnet_model.py:373] Block blocks_23 input shape: (2, 28, 28, 232)
I0102 23:07:01.539423 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 1392)
I0102 23:07:01.590515 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 1392)
I0102 23:07:01.627768 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 1392)
I0102 23:07:01.681534 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 232)
I0102 23:07:01.682088 140562995341120 efficientnet_model.py:755] block_24 survival_prob: 0.8153846153846154
I0102 23:07:01.682664 140562995341120 efficientnet_model.py:373] Block blocks_24 input shape: (2, 28, 28, 232)
I0102 23:07:01.737749 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 1392)
I0102 23:07:01.792356 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 1392)
I0102 23:07:01.829598 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 1392)
I0102 23:07:01.871776 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 384)
I0102 23:07:01.872180 140562995341120 efficientnet_model.py:755] block_25 survival_prob: 0.8076923076923077
I0102 23:07:01.872609 140562995341120 efficientnet_model.py:373] Block blocks_25 input shape: (2, 28, 28, 384)
I0102 23:07:01.924215 140562995341120 efficientnet_model.py:389] Expand shape: (2, 28, 28, 2304)
I0102 23:07:01.975001 140562995341120 efficientnet_model.py:392] DWConv shape: (2, 28, 28, 2304)
I0102 23:07:02.011464 140562995341120 efficientnet_model.py:194] Built SE se : (2, 1, 1, 2304)
I0102 23:07:02.062852 140562995341120 efficientnet_model.py:413] Project shape: (2, 28, 28, 384)
I0102 23:07:09.067493 140562995341120 det_model_fn.py:81] LR schedule method: cosine
I0102 23:07:09.566802 140562995341120 utils.py:332] Adding scalar summary ('lrn_rate', <tf.Tensor 'Select:0' shape=() dtype=float32>)
I0102 23:07:09.571111 140562995341120 utils.py:332] Adding scalar summary ('trainloss/cls_loss', <tf.Tensor 'AddN:0' shape=() dtype=float32>)
I0102 23:07:09.575444 140562995341120 utils.py:332] Adding scalar summary ('trainloss/box_loss', <tf.Tensor 'AddN_1:0' shape=() dtype=float32>)
I0102 23:07:09.579521 140562995341120 utils.py:332] Adding scalar summary ('trainloss/det_loss', <tf.Tensor 'add_3:0' shape=() dtype=float32>)
I0102 23:07:09.583730 140562995341120 utils.py:332] Adding scalar summary ('trainloss/reg_l2_loss', <tf.Tensor 'mul_14:0' shape=() dtype=float32>)
I0102 23:07:09.588010 140562995341120 utils.py:332] Adding scalar summary ('trainloss/loss', <tf.Tensor 'add_4:0' shape=() dtype=float32>)
I0102 23:07:09.594656 140562995341120 utils.py:332] Adding scalar summary ('train_epochs', <tf.Tensor 'truediv_7:0' shape=() dtype=float32>)
I0102 23:07:09.623888 140562995341120 det_model_fn.py:397] clip gradients norm by 10.000000
I0102 23:07:13.448079 140562995341120 utils.py:332] Adding scalar summary ('gradient_norm', <tf.Tensor 'clip/global_norm_1/global_norm:0' shape=() dtype=float32>)
I0102 23:07:34.193745 140562995341120 det_model_fn.py:539] restore variables from efficientdet-d3
I0102 23:07:34.193964 140562995341120 utils.py:77] Init model from checkpoint efficientdet-d3
I0102 23:07:34.204338 140562995341120 utils.py:135] Init efficientnet-b3/stem/conv2d/kernel from ckpt var efficientnet-b3/stem/conv2d/kernel
I0102 23:07:34.204492 140562995341120 utils.py:135] Init efficientnet-b3/stem/tpu_batch_normalization/gamma from ckpt var efficientnet-b3/stem/tpu_batch_normalization/gamma
I0102 23:07:34.204604 140562995341120 utils.py:135] Init efficientnet-b3/stem/tpu_batch_normalization/beta from ckpt var efficientnet-b3/stem/tpu_batch_normalization/beta
I0102 23:07:34.204746 140562995341120 utils.py:135] Init efficientnet-b3/stem/tpu_batch_normalization/moving_mean from ckpt var efficientnet-b3/stem/tpu_batch_normalization/moving_mean
I0102 23:07:34.214251 140562995341120 utils.py:127] skip class_net/class-predict/pointwise_kernel ((1, 1, 160, 5274) vs [1, 1, 160, 810]) -- shape mismatch
I0102 23:07:34.214484 140562995341120 utils.py:127] skip class_net/class-predict/bias ((5274,) vs [810]) -- shape mismatch
I0102 23:07:34.218290 140562995341120 utils.py:127] skip class_net/class-predict/pointwise_kernel/ExponentialMovingAverage ((1, 1, 160, 5274) vs [1, 1, 160, 810]) -- shape mismatch
I0102 23:07:34.222586 140562995341120 utils.py:127] skip class_net/class-predict/bias/ExponentialMovingAverage ((5274,) vs [810]) -- shape mismatch
INFO:tensorflow:Done calling model_fn.
I0102 23:07:42.476982 140562995341120 estimator.py:1175] Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
I0102 23:07:42.478458 140562995341120 basic_session_run_hooks.py:558] Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
I0102 23:07:54.542574 140562995341120 monitored_session.py:243] Graph was finalized.
2023-01-02 23:07:54.543008: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-02 23:07:55.255160: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9666 MB memory:  -> device: 0, name: TITAN V, pci bus id: 0000:04:00.0, compute capability: 7.0
INFO:tensorflow:Running local_init_op.
I0102 23:08:12.012063 140562995341120 session_manager.py:527] Running local_init_op.
INFO:tensorflow:Done running local_init_op.
I0102 23:08:12.754739 140562995341120 session_manager.py:530] Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
I0102 23:08:44.119423 140562995341120 basic_session_run_hooks.py:628] Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into ./tmp/efficientdet-d3-finetune/model.ckpt.
I0102 23:08:44.146168 140562995341120 basic_session_run_hooks.py:633] Saving checkpoints for 0 into ./tmp/efficientdet-d3-finetune/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
I0102 23:08:55.847828 140562995341120 basic_session_run_hooks.py:640] Calling checkpoint listeners after saving checkpoint 0...
2023-01-02 23:09:16.189110: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 529256448 exceeds 10% of free system memory.
2023-01-02 23:09:16.189178: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 529256448 exceeds 10% of free system memory.
2023-01-02 23:09:16.492752: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 529256448 exceeds 10% of free system memory.
2023-01-02 23:09:16.853836: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 132314112 exceeds 10% of free system memory.
2023-01-02 23:09:16.853907: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 132314112 exceeds 10% of free system memory.
2023-01-02 23:09:28.204211: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8204
INFO:tensorflow:loss = 303388.44, step = 0
I0102 23:09:32.140894 140562995341120 basic_session_run_hooks.py:266] loss = 303388.44, step = 0
INFO:tensorflow:box_loss = 0.0071479464, cls_loss = 303387.9, det_loss = 303388.25, step = 0
I0102 23:09:32.141317 140562995341120 basic_session_run_hooks.py:266] box_loss = 0.0071479464, cls_loss = 303387.9, det_loss = 303388.25, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 62...
I0102 23:10:28.943011 140562995341120 basic_session_run_hooks.py:628] Calling checkpoint listeners before saving checkpoint 62...
INFO:tensorflow:Saving checkpoints for 62 into ./tmp/efficientdet-d3-finetune/model.ckpt.
I0102 23:10:28.943264 140562995341120 basic_session_run_hooks.py:633] Saving checkpoints for 62 into ./tmp/efficientdet-d3-finetune/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 62...
I0102 23:10:33.580776 140562995341120 basic_session_run_hooks.py:640] Calling checkpoint listeners after saving checkpoint 62...
INFO:tensorflow:Loss for final step: 2.9077873.
I0102 23:10:34.618134 140562995341120 estimator.py:361] Loss for final step: 2.9077873.

@n3011
Copy link
Contributor

n3011 commented Oct 29, 2023

Use --hparams=config.yaml in the model export step to input the correct number of input classes.

python model_inspect.py --runmode=saved_model --hparams=config.yaml .....

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
2 participants
@n3011 @etale-cohomology